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, in a contact center apparatus, a method for recognizing user intent associated with user interaction with the contact center apparatus.

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.