Abstract: A method and a system for using machine learning technology to optimize a scheduling strategy for establishing contact with a customer are provided. The method includes: receiving a data set that relates to a customer account; analyzing the data set to determine at least one proposed schedule for an attempt to contact the customer; and generating, based on a result of the analysis, a report that includes the proposed schedule. The analysis may be performed by applying a machine learning model that is trained by using historical data that relates to interactions associated with the customer.

Abstract: A method for using multidimensional Hawkes processes for modeling and generating sequential events data in order to improve accuracy with respect to parameter estimation for various domains such as finance, epidemiology, and personalized recommendations is provided. The method includes: receiving information that relates to a sequence of events; modeling, based on the received information, the sequence of events by a multidimensional Hawkes process that relates to a conditional density function that includes a base intensity component and a cross-activation matrix component; defining, based on the conditional density function, a log-likelihood function that is dimensionally separable; and determining a maximum-likelihood estimation of a solution to the log-likelihood function along at least one dimension.

Abstract: A method for facilitating automated authentication of a plurality of remote terminals is disclosed. The method includes generating, via a remote terminal, a key pair, the key pair including a public key and a private key that is persisted within the remote terminal; generating, via the remote terminal, a communication request by using the key pair, the communication request including the public key; transmitting, via the remote terminal using an application programming interface, the communication request to a terminal management server; registering, via the terminal management server, the remote terminal to a terminal database by using information in the communication request; persisting, via the terminal management server, the public key from the communication request to a persistent object storage repository; and generating, via the terminal management server, a response to the communication request, the response including status information and configuration information.

Abstract: A method for performing a health check of a customized function across multiple cloud/hybrid platforms is disclosed. The method includes submitting a customized function to a health check system, which includes components on both on an on-premise network and a public cloud network. The submitted function is then uploaded to a cloud object storage. The method further includes having the customized function retrieved from the cloud object storage and scheduling an execution of the customized function according to a user defined schedule. Once the customized function is executed, the method further acquires results of the executed function and corresponding metrics and updates a health check metrics table of a metadata database with the acquired results and metrics.

Abstract: Systems and methods for card tokenization via API are disclosed. In one embodiment, in a financial institution backend comprising at least one computer processor, a method for card provisioning is provided using a digital enablement service via API may include: (1) receiving, from a digital enablement service and using a first API, a provisioning request from a digital wallet executed by an electronic device, the provisioning request identifying an account for tokenizing; (2) decisioning the provisioning request; (3) communicating the decision to the digital enablement service using a second API; (4) receiving, from the digital enablement service via a third API, a token; and (5) updating a cardholder provide with provisioned status for the token.

Abstract: Various methods, apparatuses/systems, and media for real-time data dependency management are disclosed. A processor extracts data entity events from a plurality of data sets from upstream application; identifies dependent data entities for each data entity event based on initializing a data dependency graph with parent data nodes that represent all entities and their respective child data dependencies; publishes a data dependency event for each required parent data node in the data dependency graph; publishes a data dependency ready event for a certain parent node based on determining the certain parent node is configured for event publishing and that the certain parent node's child data dependencies are available; transmits the data dependency ready event to a task orchestration service platform; and orchestrates, upon receiving the data dependency ready event by the task orchestration service platform, a process instance and executes tasks for a corresponding data entity based on the process instance.

Abstract: A method for facilitating a permission management pipeline to provide policy taxonomy governance is disclosed.

Abstract: A method and system for performing automated work assignment are disclosed. The method includes storing information of a case and operator, determining a requirement and restriction of the case, and determining, using a machine learning algorithm, availability of operator candidates matching the requirement and the restriction of the case. The method further includes assigning the case to a work bucket of the operator based on the requirement and restriction of the case, tracking a progress of the case and updating availability information of the case that is assigned. Once the case is determined to be completed, calculating attributes of performance of the operator when the case is determined to have been completed, inputting the calculated attributes of performance to the machine learning algorithm, and updating the machine learning algorithm with the calculated attributes of performance for subsequent processing by the machine learning algorithm.

Abstract: Systems and methods for using an artificial intelligence-based technique for automatic generation of travel and dining reviews are provided. The method includes: receiving a request for a review of an entity that provides a service to a user; applying a first artificial intelligence (AI) algorithm to the received request in order to generate the review of the entity; and outputting the review of the entity. The entity provides either or both of a travel-related service and a dining-related service, and as such, the entity may include a restaurant or a hotel. The AI algorithm may use a large language model and/or may be trained such that the review has a style and a tone of a Zagat review.

Abstract: Various methods, apparatuses/systems, and media for accelerating decomposition via reinforcement learning are disclosed. A processor implements a decomposition algorithm that allows a solution of a comparatively larger linear programming problems that have a special block structure; inserts a reinforcement learning agent within a framework of the decomposition algorithm; and generates, in response to inserting the reinforcement learning agent, master problem decisions in place of an NP-hard (nondeterministic polynomial time-hard) mixed-integer master problem (MIMP).

Abstract: A method and system for detecting, segmenting, and extracting salient regions in documents by using attention tracking sensors is provided. The method includes: receiving an image that corresponds to a document; receiving, from a sensor, a sequence of measurements that correspond to a human reading of the document; determining, based on the sequence of measurements, at least one region of the document as being a salient document region; demarcating the salient document region in an electronically displayable manner; and outputting a file that includes a displayable version of the document with the demarcated document region. The salient document region may include a title, a section header, and/or a table. The sensor may be an eye-tracking sensor that detects a sequence of eye-gaze positions on the document as a function of time.

Abstract: In one example, a method for prediction future network anomaly events is disclosed. The method includes generating a machine learning model for a computer communication network. Generation of the machine learning model can be by applying a historical set of time series data metrics of the computer communication network to train the machine learning model. The method may determine a data metric threshold that indicates a limit for future data metrics associated with the computer communication network. The method may analyze current data metrics associated with the computer communication network, and predict a future time when the data metrics associated with the computer communication network will meet or exceed the data metric threshold value. The method may flag the prediction of the future time to avoid a network anomaly.

Abstract: A method and a system for forecasting technology-related incidents and/or failures using artificial intelligence models and machine learning techniques are provided. The method includes: receiving a data set that relates to a system; analyzing the data set to identify a potential problem with respect to the system; and generating a report that includes a forecast that relates to the identified potential problem. The analysis is performed by applying an artificial intelligence (AI) algorithm that is trained by using historical data that relates to incidents and failures associated with the system.

Abstract: A method and a system for automated performance of capacity allocation, brokerage, placement, and provisioning of compute, network, and storage resources are provided. The method includes: receiving a first data set that relates to resource requirements of a user; retrieving, from a memory, a second data set that relates to resource availability; analyzing the first data set and the second data set in order to determine a proposed allocation of resources and a proposed timing that corresponds to the proposed allocation; and provisioning the resources to the user based on the proposed allocation and the proposed timing. A machine learning model that is trained by using historical resource allocation data may be applied to the first data set and the second data set in order to perform the analysis.

Abstract: Various methods, apparatuses/systems, and media for integration of user defined metrics of cloud hosted relational databases with cloud monitoring services are disclosed. A processor receives via a serverless function implemented within the processor a configuration file containing configuration of collection of database connection string and respective metrics query. The serverless function connects to the secrets provider to fetch a password or authorization token to connect with a plurality of different types of cloud hosted relational databases. The processor returns, in response to receiving the respective metrics query, a table with two columns where one column includes a user defined metric name and the other column includes the metric query to fetch a user defined metric value for the given user defined metric name from the databases; and automatically integrates the fetched user defined metric value of the databases with the cloud monitoring service.

Abstract: A method and a system for integration of applications on a software-as-a-service-customer relationship management (SaaS-CRM) platform using a serverless interface are disclosed. The method comprises: (1) authenticating a user device application, by an authenticator, and generating an access token; (2) sending, by the user device application, request to an application programming interface (API) gateway to integrate the user device application with the SaaS-CRM platform, wherein the request includes the access token; (3) validating, by an authorizer, the access token to generate a policy; (4) granting access to the user device application for the serverless interface; (5) retrieving, by the serverless interface, a client credential data and a private key from a digital key manager; and (6) integrating, via the serverless interface, the user device application on the SaaS-CRM platform.

Abstract: A method for collecting and processing application telemetry may include: collecting, by a telemetry insights computer program, first telemetry data from computer applications, network appliances, and hardware devices in a distributed architecture; generating, by the telemetry insights computer program and based on the first telemetry data, a CPU operating service level, a memory operating service level, and a latency service level; collecting, by the telemetry insights computer program, second telemetry data from the computer applications, the network appliances, and the hardware devices; identifying, by the telemetry insights computer program, an anomaly by comparing the second telemetry data to the CPU operating service level, the memory operating service level, and the latency service level; generating, by the telemetry insights computer program, an event for the anomaly and communicating the event to an event manager; and executing, by the event manager, an automated proactive action in response to the a

Abstract: Various methods, apparatuses/systems, and media for intelligent tracking of data are disclosed.

Abstract: Systems and methods for tracking, collecting, processing, enriching, analyzing and reporting on data in the context of cyber security investigations are disclosed. According to one embodiment, in an information processing apparatus comprising at least one computer processor, a method for conducing cyber investigations may include: (1) receiving initiation of an investigative workflow comprising contextual information including at least one of a case, a data asset, a subject, and a threat; (2) collecting digital evidence from the data asset; (3) processing the digital evidence into structured data; (4) staging and enriching the structured data; (5) analyzing the enriched structured data; and (6) generating at least one report based on the analysis.

Abstract: A method for performing preemptive scaling is disclosed. The method includes monitoring of incoming data volume outside of a cloud network at a node upstream from the cloud network, and identifying micro services required for processing of the incoming data volume. The method further includes determining a number of instances for each of the identified micro services, and contemporaneously creating the determined number of instances for each of the identified micro services in the cloud for processing of the incoming data volume.