Amdocs Development Limited

As described herein, a system, method, and computer program are provided for securing a network whose capabilities are accessible by external applications via an API. A request for one or more capabilities of a network is received from an application by an API Gateway of a platform that interfaces the network. Information associated with the request is input to a machine learning model to cause the machine learning model to predict whether the request is at least potentially malicious. The request is prevented from being sent to the network when the machine learning model predicts that the request is at least potentially malicious. The request is sent to the network when the machine learning model predicts that the request is not at least potentially malicious.

As described herein, a system, method, and computer program are provided for general intent to specific service modeling in intent-based orchestration. An intent specifying one or more parameters of a service required for a network is received. The intent is modeled as a root intent service that represents the one or more parameters of the service specified by the intent. The root intent service is decomposed into one or more specific services of a network orchestrator. The one or more specific services are caused to be instantiated in the network in order to fulfill the intent.

As described herein, a system, method, and computer program provide real-time language translation using generative artificial intelligence. An input in a first spoken language is received. The input in the first spoken language is processed, using a generative artificial intelligence model, to generate a translation of the input in a second spoken language. The translation is output.

As described herein, a system, method, and computer program are provided for batching a collection of elements in local memory for processing. A single collection of elements of data is stored in a local memory across a plurality of sub-collections. The single collection of elements is processed on a sub-collection basis, where each sub-collection is removed from the local memory upon completion of the processing of the elements in the sub-collection.

As described herein, a system, method, and computer program are provided for using malware to protect data. A dormant malware embedded in data detects that a preconfigured condition has been satisfied for self-activation. Responsive to detecting that the preconfigured condition has been satisfied, the dormant malware performs self-activation.

As described herein, a system, method, and computer program provide a test environment as a service. The service provides a user interface presenting a plurality of configurable parameters for creating a test environment. The service receives, via the user interface, user input defining configurations for one or more configurable parameters of the plurality of configurable parameters. The service creates the test environment in accordance with the configurations.

According to one aspect of the present invention there is provided a system, method, and computer program product for recovering from a network failure in a communication network using network function virtualization (NFV-based network), the method including: selecting a first network component of the NFV-based network, detecting at least one probable failure of the first network component, selecting a second network component to be used for replacing the instance of the VNF in the first network component prior to a failure of the first network component, and securing at least one resource of the selected second network component for the other instance of the VNF and maintaining, in the selected second network component, an updated copy of data associated with the instance of the VNF in the first network component.

As described herein, a system, method, and computer program are provided for using a dynamic threshold mechanism that is utilizing a set of machine learning models to auto-complete user input fields. User access to a form having a plurality of user input fields is detected. One or more of the plurality of user input fields are auto-completed over a sequence of stages, utilizing at least one machine learning model.

As described herein, a system, method, and computer program are provided for a multi-stage multi-class classification. A dataset having an imbalanced distribution of data across a plurality of classes is identified. The plurality of classes are grouped into a plurality of clusters, based on a defined criteria. A plurality of machine learning models are trained, each machine learning model of the plurality of machine learning models trained using a subset of the data in the dataset corresponding to a particular cluster of the plurality of clusters. The plurality of machine learning models are used, in stages, to predict a classification for a given input.

As described herein, a system, method, and computer program are provided for smart user alert messaging. In use, raw information associated with usage of a communication service by a customer is identified. Additionally, the raw information is processed, using artificial intelligence, to infer one or more aspects of a status of the customer. Further, one or more alerts is generated for the customer, based on the raw information and the one or more aspects of the status of the customer.