Sios Technology Corporation

Emc Corporation

Swampfox Technologies, Inc.

Avaya Inc.

Sergey A Razin

Yokuki To

Kai Chen

Yicang Wu

YinKee Yee

Robert Samuel Cooper

Derek Scott Sanders

Richard Ulmer

David Jesse Moody, Iii

Paul R Clements, Jr

Nathan Pocta

Robert S Cooper

Laura Wilson

Scott Thoman

Tracy L Marlatt

Joe Moukabaa

Rick Ulmer

Tom Hanson

James Bernard Crocker

Harold Jason Treulich

Russ Innes

Spencer Hall Robinson

Travis Goldie

Ken Smith

Embodiments of the innovation relate to, in a contact center apparatus, a method for recognizing user intent associated with user interaction with the contact center apparatus. The method includes receiving user interaction data; performing a feature extraction process on the user interaction data to generate feature data; performing an intent extraction operation on the feature data to extract topics included with the feature data; executing a classification engine on the topics extracted from the feature data to classify a user intent associated with the user interaction data; and directing the user to a corresponding working agent based upon the classified user intent

Method and apparatus for intent recognition and intent prediction based upon user interaction and behavior

In a predictor device, a method for predicting a metric of a contact center includes receiving contact center operational data associated with a time duration; training a set of algorithms and their available hyperparameters with the contact center operational data to generate a set of data models; generating a score associated with each data model of the set of data models, the score quantifying a performance of each algorithm and its available hyperparameters on the contact center operational data; identifying the data model having the largest score as a best learning model for the time duration; and generating a contact center metric prediction based on the best learning model for the time duration.

Apparatus and method for multivariate prediction of contact center metrics using machine learning

A method of training a cognitive system comprises identifying, by a cognitive training device, a performance specification associated with a cognitive device; determining, by the cognitive training device, a forecasted number of communication inputs to be received by the cognitive device to meet the performance specification; identifying, by the cognitive training device, each communication input received by the cognitive device; and when the number of communication inputs reaches the forecasted number of communication inputs, automating, by the cognitive training device, identification of intents associated with additional communication inputs received by the cognitive device. Additionally, the cognitive training device can include a statistical model employed to determine the random sampling method of the experts utilized during training of the cognitive system in order to classify the gathered input into appropriate intents while mitigating incorporation of bias.

Method and apparatus for cognitive system training with incorporation of probability and uncertainty to achieve deterministic results

Embodiments of the innovation relate to a method and apparatus for utilizing graph theory and clustering analysis to build relationships among applications and corresponding and/or dependent virtualization, cloud and datacenter infrastructure components and to derive the definition of application boundaries and corresponding infrastructure components. This allows a system administrator to manage and address complex issues within a computer infrastructure, such as availability, service level deliverable/guarantees, performance, and resource optimization.

Method and apparatus for converged analysis of application, virtualization, and cloud infrastructure resources using graph theory and statistical classification

A host device includes a controller configured to receive input/output (IO) access information associated with an IO workload, the IO access information identifying at least one of a read action and a write action associated with the IO workload over a period of time. Based upon the received IO access information associated with the storage element, the controller is configured to derive a predicted cache access ratio associated with the IO workload and relating a predicted number of cache accesses associated with the IO workload with at least one of a total number of read actions and a total number of write actions associated with the IO workload. When the predicted cache access ratio reaches a threshold cache access ratio value, the controller is configured to identify the IO workload as an IO workload candidate for caching by the host device.

Apparatus and method of performing agentless remote IO catching analysis, prediction, automation, and recommendation in a computer environment

Embodiments of the innovation relate to a host device having a memory and a processor, the host device configured to determine an anomaly associated with an attribute of a computer environment resource of the computer infrastructure. The host device is configured to correlate an object associated with the attribute of the detected anomaly with a related object of the computer infrastructure. The host device is configured to determine a root cause probability for each object of the correlated objects, the root cause probability identifying a probability of the correlated object functioning as a cause of the detected anomaly. The host device is configured to output an identification of a root object associated with the anomaly based upon the identified root cause probability.

Apparatus and method of leveraging semi-supervised machine learning principals to perform root cause analysis and derivation for remediation of issues in a computer environment

A host server is configured to receive information related to metrics and configurations associated with computer resources of a computer infrastructure, derive and resolve the information into capacity, performance, reliability, and efficiency, as related to attributes associated with the computer resources, including compute attributes such as application, virtual machine (VM) attributes, storage attributes, and network attributes. The host server provides the metrics and attributes in a matrix configuration as a graphical user interface (GUI) on an output device, such as a display. The GUI is configured to provide a user with a single point of view into the computer infrastructure by converging application, compute, storage, and network attributes into capacity, performance, reliability, and efficiency concepts. With such a configuration, the GUI allows the end user to readily review the environments for potential issues in a time efficient manner, as well as solutions provided by the GUI.

Interface for orchestration and analysis of a computer environment

Embodiments relate to a method for managing and analyzing a computer environment. The method includes receiving, by the host device, a set of data elements from at least one computer environment resource of the computer infrastructure, each data element of the set of data elements relating to an attribute of the at least one computer environment resource. The method includes applying a system analysis function to the set of data elements to characterize a dataset specification associated with the set of data elements. The method includes receiving, by the host device, a user-selected policy threshold criterion based on the dataset specification and providing the user-selected policy threshold criterion to the semi-supervised learning algorithm as a parameter. The method includes adjusting a boundary of the dataset specification of the set of data elements, as associated with the user-selected policy threshold criterion, based on a behavioral change of the computer infrastructure.

Apparatus and method for leveraging semi-supervised machine learning for self-adjusting policies in management of a computer infrastructure

Session information is received in a processing device and processed to generate a message in a message queuing protocol format. The resulting message is provided to a message queuing protocol server. The session information may comprise at least a portion of a session initiation protocol (SIP) message, and may be received in a SIP component, which may comprise a SIP endpoint. In processing the session information to generate the message in the message queuing protocol format, at least a portion of the session information may be incorporated into an advanced message queuing protocol (AMQP) message that is transmitted to an AMQP server. The SIP component may comprise a user agent coupled to an AMQP message listener and an AMQP message sender. The SIP component may also receive an AMQP message from the AMQP server, process the message to generate session information, and provide the session information to a user device.

Processing device having session component with integrated support for message queuing protocol

A common information model (CIM) message received in a processing device of an information processing system is transformed to a message in a designated message queuing protocol format, such as an advanced message queuing protocol (AMQP) message format. The transformed message is then posted or otherwise provided to a message queuing protocol server. The CIM message may comprise, for example, a CIM notification received in a CIM client from a CIM server, responsive to an indication subscription from the CIM client. The transformation of the CIM message to the message in the designated message queuing protocol format may more particularly involve, for example, transforming a CIM notification associated with the CIM message to a CIM instance object, transforming the CIM instance object to an alerting object, and transforming the alerting object to an AMQP message. One or more of these operations may be performed in a CIM transform module.

Transformation of common information model messages

A Session Initiation Protocol (SIP) proxy device associated with a computer system is configured to provide a management device with the ability to manage a computer system using the Session Initiation Protocol. Because SIP is a standard communication protocol, a system manager can configure both a management device and a system component of the computer system to communicate with each other while minimizing integration issues. Additionally, SIP provides bidirectional communication between the management device and the system component that allows the system manager to request and receive real-time computer system status as well as associated presence information when managing the computer system. Furthermore, SIP can be configured as a secure communication protocol that encrypts communications exchanged between two or more devices, thereby allowing the system manager to securely manage and obtain computer system status independent of the manager's physical or geographic location.

System management via session initiation protocol

A method, apparatus and computer program product for providing automatic resource distribution is presented. A number of ports of a resource to hold (H) as needed for each Multi Media Platform (MPP) is determined. Further, the maximum number of ports of a resource that can be acquired (M)&#x2014;M&#x2212;H of which are borrowed from other MPPs&#x2014;for each MPP is also determined. The ports are then allocated for an MPP from at least one server in accordance with the number of ports to hold as needed and the number of ports that can be borrowed.

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Columbia, SC, United States of America

Sergey A Razin