Abstract:
A method for detecting and categorizing topics in a plurality of interactions includes: extracting, by a processor, a plurality of fragments from the plurality of interactions; filtering, by the processor, the plurality of fragments to generate a filtered plurality of fragments; clustering, by the processor, the filtered fragments into a plurality of base clusters; and clustering, by the processor, the plurality of base clusters into a plurality of hyper clusters.
Abstract:
A method for generating a predictor of customer behavior for a contact center includes: collecting, by a processor, data from a plurality of different applications of the contact center, the data being stored in a plurality of different formats, the data corresponding to a plurality of recorded interactions between a plurality of customers and the contact center; converting, by the processor, the data from the plurality of different formats into a common format; generating, by the processor, a plurality of customer models for the customers by, for each customer of the customers: identifying, from the data from the plurality of different applications, identified data associated with the customer; and aggregating the identified data in an individual customer model of the plurality of customer models, the individual customer model being associated with the customer; and generating, by the processor, a predictor in accordance with the customer models.
Abstract:
A system and method for routing interactions to contact center agents. The system is adapted to concurrently identify a plurality of interactions waiting to be routed, and identify a plurality of candidate agents viable for handling the plurality of interactions. The system is also adapted to calculate a predicted wait time associated with each of the candidate agents. For each agent of the plurality of candidate agents, the system is adapted to estimate an expected value to be obtained by routing each of the plurality of the interaction to the agent. The expected value is a function of the predicted wait time. The system is further adapted to assign each of the plurality of the interactions to one of the plurality of candidate agents based on the estimated reward, and signal a routing device for routing each of the plurality of the interactions to the agent assigned to the interaction.
Abstract:
A method for extracting, from non-speech text, training data for a language model for speech recognition includes: receiving, by a processor, non-speech text; selecting, by the processor, text from the non-speech text; converting, by the processor, the selected text to generate converted text comprising a plurality of phrases consistent with speech transcription text; training, by the processor, a language model using the converted text; and outputting, by the processor, the language model.
Abstract:
A method for predicting a speech recognition quality of a phrase comprising at least one word includes: receiving, on a computer system including a processor and memory storing instructions, the phrase; computing, on the computer system, a set of features comprising one or more features corresponding to the phrase; providing the phrase to a prediction model on the computer system and receiving a predicted recognition quality value based on the set of features; and returning the predicted recognition quality value.
Abstract:
A request is received from a communications device to execute an interaction site. A request is transmitted to the automated response system. First instructions that provide one or more steps of the multi-step communication flow between the communications device and the automated response system are received from the automated response system. In response to determining that the request is for the voice-interaction with the interaction site, second instructions that provide the one or more steps through a voice interaction with a user of the communications device are determined and transmitted to the communications device. In response to determining that the request is for the visual-interaction with the interaction site, third instructions that provide the one or more steps through a visual interaction with the user of the communications device are determined and transmitted to the communications device.
Abstract:
A method for generating a dialogue tree for an automated self-help system of a contact center from a plurality of recorded interactions between customers and agents of the contact center includes: computing, by a processor, a plurality of feature vectors, each feature vector corresponding to one of the recorded interactions; computing, by the processor, similarities between pairs of the feature vectors; grouping, by the processor, similar feature vectors based on the computed similarities into groups of interactions; rating, by the processor, feature vectors within each group of interactions based on one or more criteria, wherein the criteria include at least one of interaction time, success rate, and customer satisfaction; and outputting, by the processor, a dialogue tree in accordance with the rated feature vectors for configuring the automated self-help system.
Abstract:
A method for configuring an automated self-help system based on prior interactions between a plurality of customers and a plurality of agents of a contact center includes: recognizing, by a processor, speech in the prior interactions between customers and agents to generate recognized text, the recognized text including a plurality of phrases, the phrases being classified into a plurality of clusters; extracting, by the processor, a plurality of sequences of clusters, each of the sequences of clusters corresponding to the phrases of one of the prior interactions; filtering, by the processor, the sequences of clusters based on a criterion; mining, by the processor, a preliminary dialogue tree from the sequences of clusters; invoking configuration of the automated self-help system based on the preliminary dialogue tree; and outputting a dialogue tree for configuring the automated self-help system.
Abstract:
A method for configuring an automated, speech driven self-help system based on prior interactions between a plurality of customers and a plurality of agents includes: recognizing, by a processor, speech in the prior interactions between customers and agents to generate recognized text; detecting, by the processor, a plurality of phrases in the recognized text; clustering, by the processor, the plurality of phrases into a plurality of clusters; generating, by the processor, a plurality of grammars describing corresponding ones of the clusters; outputting, by the processor, the plurality of grammars; and invoking configuration of the automated self-help system based on the plurality of grammars.
Abstract:
A system includes a contact center to provide an interaction between a customer and agent. A forms manager of the contact center generates a question for an evaluation form. A workforce management server connects with the forms manager, the workforce management server to schedule a work time for the agent. The workforce management server to schedule the forms manager to generate the evaluation form when the agent is working.