Nice Ltd

Nice Systems Ltd.

Other

Gennadi Lembersky

Yuval Shachaf

Hila Kneller

Roy Klein

Natan Katz

Lior Ben Eliezer

Moshe Wasserblat

David Geffen

Maor Nissan

Ronny Bretter

Jeffrey Stern

Neta Rosenfeld

Mahesh Bairagi

Amol Anwade

Borys Havdan

Maya Gorodetsky

Yevhenii Lukin

Adi Pony

Sourav Rath

Animesh Udawat

Shay Diner

A system and method for classification of voice samples to genuine voice samples or spoofing voice samples may include: extracting a set of features from each of a plurality of voice samples, each voice sample labeled as genuine or spoof; training a neural network having a plurality of nodes organized into layers, with links between the nodes, wherein each link comprises a weight, with the sets of features, by adjusting at least one of the weights using a loss function that comprises a regulation factor, wherein the regulation factor is set to zero for voice samples labeled as genuine and is proportional to the prediction of the neural network for data samples labeled as spoofing.

System and method for spoofing detection

Coaching systems and methods, and non-transitory computer readable media, include analyzing an agent's interactions to identify knowledge gaps and specific topics where an agent has difficulties. An algorithm uses bootstrap sampling to verify that an agent's scores are significantly different from other agents' scores. The algorithm further uses a mutual information score to find topics that are associated with interactions having a high knowledge gap score.

System and methods to derive knowledge base article relevancy score

A system and methods are provided to analyze audio signals from an incoming voice call. The system includes a processor and a computer readable medium operably coupled thereto, to perform voice analysis operations which include receiving a first audio signal comprising a first audio waveform of a first speech between at least two users during the incoming voice call, accessing speech segment parameters for analyzing the audio signals, determining one or more talk-over segments in the first audio waveform using the speech segment parameters, extracting audio features from each of the one or more talk-over segments, determining, using a machine learning (ML) model trained for interruption analysis of the audio signals, whether each of the one or more talk-over segments are a negative interruption or a non-negative interruption based on the audio features, and determining whether to output a first notification for the negative interruption or the non-negative interruption.

Identification and classification of talk-over segments during voice communications using machine learning models

A system and method for calculating a journey excellence score (JES) for a customer journey in a contact center, including, during a training phase: representing each of a plurality of tagged customer journeys by a list of attributes and a value associated with each of the attributes, wherein each of the tagged customer journeys is tagged with respect to a dissatisfaction indicator, and training a model using the plurality of tagged customer journeys, based on the associated values and tags, to generate model weights; and at runtime: obtaining the customer journey; representing the customer journey by the list of attributes and a value associated to each of the attributes; and calculating the JES for the customer journey by applying the model weights to the associated values.

System and method for generating journey excellence score

Calculating a score for a chain of interactions in a call center may include: during a first training phase, train a first model which, given an interaction and interaction metadata, predict an initial estimated customer satisfaction score; during a second training phase, train a second model which, given an interaction and interaction metadata, text and metadata of an immediately preceding interaction in a chain of interactions, and features of the chain, predict a refined estimated customer satisfaction score; and during an inference phase: given a chain of interactions and metadata of each interaction, compute an initial estimated customer satisfaction score for each interaction using the first model; beginning with a second interaction in the chain and metadata of each interaction, compute a refined estimated customer satisfaction score for each interaction using the second model; combine the interaction scores into a combined customer satisfaction score; and output the score.

System and method for calculating a score for a chain of interactions in a call center

A method and system for analyzing interactions (for example between a person and an organization) may include for a number of series of interactions (which may be termed journeys), each interaction represented by a channel and a reason, iterating over the series of interactions by selecting initial information including an interaction subset including one or more interactions, and if there is a set of interaction series among the series of interactions meeting certain conditions, adding an interaction to the interaction subset and determining in a recursive fashion if for the interaction subset, there is a subset among the set of interaction series meeting the conditions which also meet the conditions. Conditions may include a threshold average rating for the set of interaction series, and a threshold number of interaction series in which the interaction subset is found.

Systems and methods for finding an interaction subset within a set of interactions

A system and method for creating input data to be used to train a conversational bot may include receiving a set of conversations, each conversation including sentences, classifying each sentence into a dialog act taken from a number of dialog acts, for each set of sentences classified into a dialog act, clustering the set of sentences into clusters based on the content (e.g. text) of the sentences, each cluster having a cluster name or label, and generating a language model based on the cluster labels. Slots may be identified in the sentences based in part on the dialog act classifications. A bot may be trained using data such as the slots, language model, and clusters.

System and method for creating data to train a conversational bot

Systems and methods for measuring the effectiveness of an agent coaching program calculate a rate of change in a first Key Performance Indicator for a first agent in a first coaching program during a period of time; select a control group of agents in which agents in the control group of agents were not exposed to the first coaching program; calculate an average rate of change in the first Key Performance Indicator for the control group of agents during the period of time; and calculate a first coaching impact of the first coaching program on the first Key Performance Indicator for the first agent relative to the average rate of change in the first Key Performance Indicator for the control group of agents during the period of time.

Systems and methods for measuring the effectiveness of an agent coaching program

Systems and methods of mapping a customer journey in an interactive voice response (IVR) system to a contact reason from a contact reasons list: receive an IVR log comprising a plurality of customer journey entries, wherein each customer journey entry comprises a sequence of one or more menu identifiers; generate an embedding vector for each menu identifier; filter one or more menu identifiers from a menu identifier list, wherein the menu identifier list comprises all menu identifiers contained in the IVR log; cluster one or more remaining menu identifiers from the menu identifier list into one or more clusters, based on the embedding vector of each menu identifier; map each cluster to a contact reason; and create a rule that categorizes a newly received IVR sequence based on a cooccurrence of at least one menu identifier in the newly received IVR sequence and in a given cluster.

System and method for mapping a customer journey to a category

Systems and methods for mapping a customer journey in an interactive voice response (IVR) system to a category from a categories list, build a directed graph based on one or more sample IVR customer journeys; generate a black list based on the directed graph; for a given customer journey: filter one or more non-informative menus from the plurality of available menus based on the black list; concatenate the respective associated menu prompt of each menu that was not filtered, and one or more user responses to one or more menu prompts from the series of interactions in the given customer journey, into a concatenated word string; calculate a similarity score between the concatenated word string and a category name of each category from the categories list; and map the given customer journey to the category whose category name produces the highest similarity score.

Methods and systems for optimization of interactive voice recognition (IVR) system processes are provided. One or more desired optimization parameters can be determined based on an IVR transaction log that includes a plurality of IVR journeys. The IVR journeys can be filtered, transformed into vectors and/or clustered.

Systems and methods for optimization of interactive voice recognition systems

A method, computerized apparatus and computer program product for determining script compliance in interactions, the method comprising: receiving one or more indexed audio interaction; receiving a text representing a script; automatically extracting two or more key terms from the script; automatically generating a query representing the script, comprising: receiving one or more constraint associated with the at least two key terms; and determining spotted key terms of the key terms that appear in the indexed audio interactions; determining complied constraints based on a number of words difference between two key terms of the at least two key terms; and determining a relevance score for each of the indexed audio interactions, based on the spotted key terms and the complied constraints.

Script compliance in spoken documents based on number of words between key terms

A system and method for analysis of interactions with a customer service center, comprising receiving a plurality of customer service interactions, receiving a word cloud computed for the plurality of customer service interactions, consolidating similar or synonymous words into word sets, constructing a weighted graph of the word sets, generating a query for the interaction topic based on a corresponding subset of word sets, selecting at least one representative interaction from the retrieved customer service interactions, and displaying at least a portion of the representative interaction for the selected identified topic.

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Nesher, Israel

Gennadi Lembersky