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 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, 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 method for generating a dialog 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 dialog tree in accordance with the rated feature vectors for configuring the automated self-help system.

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 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 dialog tree from the sequences of clusters; invoking configuration of the automated self-help system based on the preliminary dialog tree; and outputting a dialog tree for configuring the automated self-help system.

Abstract: A method for tracking known topics in a plurality of interactions includes: extracting, by a processor, a plurality of fragments from the plurality of interactions; initializing, by the processor, a collection of tracked topics to an empty collection; computing, by the processor, a similarity between each fragment of the fragments and each of the known topics; and adding, by the processor, a known topic of the known topics to the tracked topics in response to the similarity between a fragment and the known topic exceeding a threshold value.

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 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 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 method for tracking known topics in a plurality of interactions includes: extracting, by a processor, a plurality of fragments from the plurality of interactions; initializing, by the processor, a collection of tracked topics to an empty collection; computing, by the processor, a similarity between each fragment of the fragments and each of the known topics; and adding, by the processor, a known topic of the known topics to the tracked topics in response to the similarity between a fragment and the known topic exceeding a threshold value.

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.