Abstract: 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.

Abstract: Embodiments of the innovation relate to a data extraction device, comprising a controller having a processor and memory. The controller is configured receive an unstructured data file comprising a set of documents; apply the unstructured data file to a document identification model to identify a data element identifier and an associated data element of each document of the set of documents; apply an optical character recognition engine to the identified data element identifier and associated identified data element to generate a structured data element identifier and an associated structured data element, the structured data element identifier and the associated structured data element configured as machine-identifiable characters; embed the structured data element identifier and associated structured data element as metadata with the unstructured data file; and store the unstructured data file and metadata in a database.

Abstract: In a contact center apparatus, a method for generating recognized text based upon verbal data, comprising: receiving verbal data from a user; applying a natural language understanding engine the verbal data to generate phonetic representation text, the phonetic representation text configured as a phonetic representation of the verbal data; applying a language reinforcement engine to the phonetic representation text to generate recognized text, the recognized text identifying a word associated with the verbal data; and directing the user to a contact center resource based upon the recognized text.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the innovation relate to, in a contact center apparatus, a method for identifying an incident associated with a service provided by a service provider. The method comprises: receiving real-time data from a data source, the real-time data identifying a reported incident associated with a service; applying the real-time data to an incident recognition model, the incident recognition model configured to identify a state of the service provided by the service provider; in response to applying the real-time data to an incident recognition model, identifying one of an absence of a service incident and a presence of a service incident associated with the service provider; and in response to identifying the presence of the service incident, outputting an incident notification to a contact center agent device.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for training and deploying machine-learned models to predict medical codes are provided. In one example embodiment, a computing system can train machine-learned model(s) to predict medical codes based on training medical data. The machine-learned models can then be utilized to predict medical codes based on medical data. This can include a two stage model architecture. The two-stage architecture can include, for example, a unbiased machine-learned model trained to predict medical codes and a biased machine-learned model trained to revise the predicted medical codes according to a specific domain.

Abstract: 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.

Abstract: 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.

Abstract: In a host device, a method for stabilizing a data training set comprises generating, by the host device, a data training set based upon a set of data elements received from a computer infrastructure; applying, by the host device, multiple iterations of a classification function to the data training set to generate a set of data element groups; dividing, by the host device, the set of data element groups resulting from the multiple iterations of the clustering function into multiple time intervals; for each time interval of the multiple time intervals, deriving, by the host device, a maximum threshold and a minimum threshold for each data element groups of the set of data element groups included in the time interval; applying an order statistic function to the maximum thresholds and the minimum thresholds for each time interval; and identifying a relative variability among the ordered maximum thresholds.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the innovation relate to an apparatus and method of behavior forecasting in a computer infrastructure. The method includes, for each computer environment resource of the computer infrastructure having a related attribute, deriving a set of clusters for the related attribute of each associated computer environment resource and detecting a learned behavior boundary associated with each set of clusters for each associated computer environment resource. The method includes combining the learned behavior boundaries associated with each set of clusters for each associated computer environment resource to generate a resulting attribute pattern associated with the computer infrastructure; applying an attribute pattern threshold to the resulting attribute pattern; and identifying a forecasted behavior of the computer infrastructure based upon the application of the attribute pattern threshold to the resulting attribute pattern.

Abstract: In a host device, a method for performing an anomaly analysis of a computer environment includes applying a learned behavior function to a data training set and to a set of data elements received from at least one computer environment resource to define at least one learned behavior boundary relative to at least one cluster of data elements of the data training set; applying a sensitivity function to the at least one cluster to define a sensitivity boundary relative to at least one learned behavior boundary, the sensitivity boundary related to a variance associated with the at least one cluster and to a mean value of the at least one cluster; and identifying a data element of the set of data elements as an anomalous data element when the data element of the set of data falls outside of the sensitivity boundary.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the present innovation relate to a host device that includes a configured to receive a set of data elements from a computer infrastructure, the set of data elements relating to at least one attribute of at least one computer environment resource of the computer infrastructure. The controller is configured to assign each data element of the set of data elements to a data retention location based upon a time statistic identifier associated with each data element of the set of data elements and to compare a training data set and the data elements associated with a selected data retention location to detect a data anomaly associated with the set of data elements. In response to detecting the data anomaly associated with the data elements associated with the selected data retention location, the controller is configured to generate a data anomaly notification.