Abstract: As described herein, a system, method, and computer program are provided for blockchain-based entity group management. An instance of a blockchain is maintained for each entity group of a plurality of defined entity groups. Further, the instance of the blockchain maintained for each entity group of the plurality of defined entity groups is utilized to manage group membership for the entity group, and control access by members of the entity group to a plurality of services having functionality configured for the plurality of defined entity groups.

Abstract: As described herein, a system, method, and computer program are provided for presenting tree data structures in tables. In use, a forest data structure storing data is identified. Additionally, the forest data structure is converted to a plurality of matrices. Further, the plurality of matrices are used to present the data in a table.

Abstract: As described herein service brokers are used to manage the lifecycle of backing services. In use, a service catalog of backing services available for use by an application is provided. Further, one or more service brokers are used to manage a lifecycle of the backing services.

Abstract: As described herein, a system, method, and computer program are provided for orchestrating resources in a 5G network based on user equipment behavior analytics. A policy control function in the 5G network collects analytic information associated with past behavior of user equipment in a 5G network. The policy control function predicts future behavior of the user equipment in the 5G network, based on the analytic information. The policy control function generates one or more policy decisions based on the future behavior of the user equipment, wherein the one or more policy decisions cause an orchestration function of the 5G network to orchestrate resources within the 5G network for use by the user equipment.

Abstract: As described herein, a system, method, and computer program are provided for an exactly once messaging protocol in a publish-subscribe messaging system. A consumer (i.e. subscriber) of a publish-subscribe messaging system is identified. For each required consumer, and for each subscribed topic of the consumer, a new dedicated consumer is created for the topic in the publish-subscribe messaging system. The new dedicated consumer is used to ensure that messages published to the topic are delivered exactly once to each associated consumer.

Abstract: As described herein, a system, method, and computer program are provided for securing container images. In use, a request to access a container image is identified. In response to the request, a digest of the container image is retrieved. The digest is validated according to an execution and business context. A response to the request is provided, based on a result of the validating.

Abstract: As described herein, a system, method, and computer program are provided for AWS autoscaling of Tuxedo systems. In use, an AWS cloud based deployment of a Tuxedo system is identified. Further, autoscaling of the Tuxedo system is provided in accordance with an autoscaling configuration of the AWS, using a Tuxedo registrar that maps AWS EC2 DNS names or internet protocol (IP) addresses with Tuxedo-compliant names capable of being used by the Tuxedo system.

Abstract: As described herein, a system, method, and computer program are provided for generating a context visualization during test automation. In use, during automated testing of a user interface application, an inspection of an element within a user interface of the user interface application is detected. Additionally, in response to detecting the inspection of the element, a visualization of the element within the user interface is generated. Further, the visualization of the element within the user interface is presented with information associated with the inspection of the element.

Abstract: As described herein, a system, method, and computer program are provided for a model driven Non-RT RIC design for O-RAN management and orchestration. One or more features of one or more onboarded Non-RT RIC applications are determined. During run-time of the Non-RT RIC, a set of components are deployed for the Non-RT RIC based on the one or more features of the one or more onboarded Non-RT RIC applications.

Abstract: As described herein, a system, method, and computer program are provided for using metric valuations in anomaly scoring. In use, a score calculated for a metric used for anomaly detection is identified. Additionally, the score is weighted, based on a valuation defined for the metric, to form a weighted score. Further, at least one action is caused to be performed, based on the weighted score.

Abstract: As described herein, a system, method, and computer program are provided for intelligent self-healing optimization for fallout reduction. A set of self-healing rules are stored that are configured to provide resolutions to failures detected in a computer process. Data associated with use of the self-healing rules is collected. The data is processed using a machine learning model to generate one or more recommendations for optimizing the set of self-healing rules. The one or more recommendations are output.

Abstract: As described herein, a system, method, and computer program are provided for defect resolution. Information associated with a defect detected in a computer system is received. The information is processed, using a first machine learning model, to predict a source of the defect. The information and the source of the defect are processed, using a second machine learning model, to predict one or more parameters for handling the defect. One or more actions are caused to be performed to resolve the defect, based on the predicted one or more parameters for handling the defect.

Abstract: As described herein, a system, method, and computer program are provided for unobtrusive propagation of solutions for detected incidents in computer applications. A plurality of incidents detected in association with execution of at least one application are identified, the plurality of incidents being detected for the purpose of being addressed with a solution. The plurality of incidents are aggregated to identify one or more unique incidents. For each unique incident of the one or more unique incidents, at least one of machine learning or natural language processing is used to generate a plurality of ranked solutions for the unique incident, a selection of one solution of the plurality of ranked solutions is received, and the selected solution is deployed to one or more environments on which the unique incident was detected, utilizing a blockchain.

Abstract: A system, method, and computer program are provided for protecting against unintentional deletion of an eSIM from a mobile device. In use, deletion of an eSIM from a mobile device is detected. Additionally, definitions of predefined scenarios indicative of unintentional deletions of eSIMs are accessed. Further, the detected deletion is analyzed, using the definitions, to determine that the detected deletion is unintentional. Still yet, a proactive care action is caused to be performed to address the unintentional deletion of the eSIM from the mobile device.

Abstract: As described herein, a system, method, and computer program provide a computer attack response service. In use, a notification is received that a transfer of at least one electronic file to a computing device has been detected as a potential incoming threat to the computing device. Responsive to the receiving the notification, at least one honeypot is created. Additionally, data within the at least one electronic file is accessed, using the at least one honeypot. Responsive to accessing the data within the at least one electronic file, activity associated with the incoming threat is monitored.

Abstract: As described herein, a system, method, and computer program are provided for dynamically generating assistance information for customer service agents. In use, presence of a customer at a physical retail store is identified. Additionally, information describing the customer is processed, using a machine learning model, to determine an expected outcome of an interaction with the customer occurring within the physical retail store. It is then determined that the customer is to be assisted by a customer service agent. Further, assistance information for the customer service agent is dynamically generated, based at least in part on the expected outcome of the interaction with the customer.

Abstract: A system, method, and computer program are provided for processing a billing item. In use, a first dataset is collected including a plurality of records. The first dataset includes customer records, billing records (with billing item(s)) for each of the customers, and call incident records (with calling customer identification, billing record identification, and billing item identification). Additionally, a first AI-model is trained using the first dataset to recognize at least one pair of a first customer type and a first billing item type, and an associated first probability that such pair results in a call to the call-center. Further, a second dataset is collected including new billing records. The first AI-model is used to detect at least one billing record in the second dataset having probability higher than a redefined threshold probability that the customer associated with the billing record will call a call-center.

Abstract: As described herein, a machine learning system, method, and computer program are provided for evaluation of customer service agents. In use, presence of a customer at retail store is identified. Additionally, information associated with the customer is processed, using a machine learning model, to determine an expected outcome of an interaction with the customer occurring within the retail store. Further, after the interaction by an agent of the retail store with the customer, an actual outcome of the interaction is determined. Still yet, the agent is evaluated by comparing the actual outcome with the expected outcome. A result of the evaluation is then output, such as for use in assigning the agent to future in-store customers.

Abstract: As described herein, a system, method, and computer program are provided for anomaly detection in time-series data with mixed seasonality. In use, time-series data of a mixed seasonality type is received. Additionally, the time-series data is segmented, by a defined unit of time, to form a plurality of time-series data segments. Further, the plurality of time-series data segments are processed to determine one or more patterns across the plurality of time-series data segments. Still yet, the one or more patterns are stored and used to perform pattern matching for an input time-series.

Abstract: As described herein, a system, method, and computer program are provided for using shared customer data and artificial intelligence to predict customer classifications. A first system of a first business entity receives an artificial intelligence model generated using output of a secure multi-party computation applied to: a first schema of first customer data stored by the first system, and a second schema of second customer data stored by a second system of a second business entity. Additionally, the first system executes the artificial intelligence model on the first customer data stored by the first system to generate a predictor, the predictor configured to receive input and process the input to predict a classification for the input. Further, the first system distributes the predictor for use by the second system of the second business entity to predict at least one classification for the second customer data.