Amdocs Development Limited

Gad Yitzhak Weissman

Nimrod Sandlerman

Marina Ankri

Shmuel Ur

Eyal Felstaine

Nir Fattal

Ofer Hermoni

Liron Shtraichman

As described herein, a system, method, and computer program are provided for processing a dataset. In one embodiment, an AI-model to inference the dataset is selected. A plurality of inferencing engines each using the AI-model are initiated, where a number of the inferencing engines that are initiated is calculated according to a predetermined time constraint. The dataset is divided between the plurality of inferencing engines.

System, method, and computer program for orchestrating time-limited AI-inferencing

As described herein, a system, method, and computer program are provided for creating a new class of instances. A first dataset comprising a plurality of instances is obtained. A first AI engine is used to classify the plurality of instances into a plurality of classes. Each class of instances of the first dataset is associated with a respective action having a positive result or a lack of the positive result. A class is selected and the respective action is executed on instances of the class. Instances of the selected class for which the respective action resulted in a lack of the positive result are collected. The collection of instances are used to train an AI-model, and/or a second AI engine is used to classify the collection of instances, and/or a plurality of artificial instances are synthesized based on the collection of instances.

System, method, and computer program for identifying a hidden class

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.

System, method, and computer program for preempting bill related workload in a call-center

A system, method, and computer program product are provided for verifying virtual network function (VNF) package and/or network service definition integrity. In use, a system identifies a virtual network function package or a network service definition for performing integrity verification. The system computes a unique identifier of the VNF package or the network service definition that allows verification of an integrity of the VNF package or the network service definition. The system stores the unique identifier in a blockchain or a shared database. The system provides the VNF package or the network service definition to an entity such that the entity is capable of verifying the integrity of the VNF package or the network service definition by using the unique identifier of the VNF package or the network service definition from the blockchain or the shared database.

System, method, and computer program for verifying virtual network function (VNF) package and/or network service definition integrity

Systems and methods transfer execution of tasks executed on behalf of client devices between edge nodes operated by different operators. A request is received at a destination edge node deployed at a network edge to provide computing services to a client device currently served by an origin edge node. The origina edge node and the destination edge node are operated by different operators. An edge identifier of the origin edge node and task identifier(s) of task(s) executed by the origin edge node for the client device are received. The origin edge node is communicated with to obtain task information relating to the task(s) in one or more registries shared by the first and second operators to map task identifier(s) to respective tasks. A connection is made to the client device the task(s) are executed for the client device using the task information.

Handshake of application execution between edge nodes

A system, method, and computer program product are provided for implementing various device decisions regarding edges/towers. The method comprises one or more of: choosing a single best edge or tower for a device; splitting workloads between multiple edges or towers; and controlling multiple smart devices and deciding how to split a workload between them.

Growing community of inventors

Hod Hasharon, Israel

Gad Yitzhak Weissman