Nice Ltd

Hila Weisman

Raanan Gonen

Peter Izsak

Victor Shafran

Rana Daoud

Hila Kneller

Inna Achlow

A system and method for generating an agent behavioral analytics including transcribing an incoming call to produce a call transcription; and using a trained convolutional neural network (CNN) to produce behavioral labels for the agent in the incoming call for behavioral metrics, based on the call transcription. The CNN may include an embedding layer to convert words in the call transcription into vectors in a word embedding space; a convolution layer to perform a plurality of convolutions on the vectors and to generate vectors of features; a pooling layer to concatenate the vectors of features to a single vector by taking a maximum of each feature generated by the convolution layer; and a classification layer to produce grades of the agent in the incoming call for the set of attributes or behavioral metrics, based on the single vector generated by the pooling layer.

System and method for performing agent behavioral analytics

A system and method for generating an agent behavioral analytics including extracting text-based features, sentiment-based features, and prosody-based features from tagged calls; training a machine learning behavioral model, based on the text-based features, the sentiment-based features, and the prosody-based features extracted from the tagged calls and an at least one score associated with an at least one behavioral metric of the tagged calls, to produce a trained machine learning behavioral model; extracting text-based features, sentiment-based features, and prosody-based features from an incoming call; and using the trained machine learning behavioral model to produce an at least one behavioral label for the agent in the incoming call for the at least one behavioral metric, based on the text-based features of the incoming call, the sentiment-based features of the incoming call and the prosody-based features of the incoming call.

Call center agent performance scoring and sentiment analytics

Systems and methods for text normalization in a plurality of noisy channels receive a text entry and channel origin data of the text entry; determine whether the text entry matches an in-vocabulary (IV) entry or whether the text entry is an out-of-vocabulary (OOV) entry; if the text entry is determined to have a matching IV entry, output the matching IV entry, and if the text entry is determined to be an OOV entry, implement a channel-specific error-type adapter framework based on the channel origin data, wherein the channel-specific error-type adapter framework is optimized for a specific channel from which the text entry originated; normalize the text entry using the channel-specific error-type adapter framework; and output one or more candidate normalized forms of the text entry.

System and method for text normalization in noisy channels

A method for determining prominent keyphrases in interactions, comprising, receiving keyphrases of numerous interactions received via a plurality of channels that comprise apparatuses for conveying the interactions according to the media thereof, quantitatively evaluating each keyphrase by a plurality of different metrics thereby yielding a corresponding plurality of values, and linearly combining the plurality of the values with a corresponding plurality of coefficients associated with a channel via which an interaction having said keyphrase is received, thereby providing a score of said keyphrase, and based on a condition related to the scores of the keyphrases, selecting at least one keyphrase as an at least one prominent keyphrase, wherein the method is at least partially performed by an at least one computerized apparatus configured for interfacing with the apparatuses of the plurality of the channels.

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Tel Aviv, Israel

Hila Weisman