Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computerized system and method for allocating multi-functional or multi-feature resources (which may handle multiple functions or tasks, e.g., simultaneously) for a plurality of time intervals, including: transforming an initial allocation matrix (which may associate each resource with a single function, task, or feature - and may not address simultaneous handling of tasks or task types by the resources) into an updated allocation matrix, where the updated allocation matrix includes a plurality of feature matrices describing different multi-feature resources to be allocated; predicting, using a machine learning (ML) model, expected service metrics for the updated allocation matrix; and providing a final allocation matrix based on the expected service metrics. Embodiments may perform iterative calculations and/or transformations of data to improve allocation matrices and provide a final allocation matrix for which predicted service metrics correspond to required or optimal service metrics.

Abstract: Methods and systems for, upon receipt of a second computer data stream, predicting a change in processing a first computer data stream, include: receiving, at a computing device, the first computer data stream; generating a first data sequence comprising a time of receipt of the first computer data stream; receiving the second computer data stream; generating a second data sequence comprising a time of receipt of the second computer data stream; sending the first and second data sequences to a prediction model; predicting, by the prediction model, at least one change in at least one metric associated with processing the first computer data stream, the predicted change based at least in part on the first and second data sequences; and sending, by the prediction model, to the computing device, the at least one change in the at least one metric associated with processing the first computer data stream.

Abstract: A computerized-method for automatically pointing on an influencer on a measured performance change is provided herein. The method may include: receiving one or more metrics from a user to construct a Key Performance Indicators (KPI); retrieving data related to one or more agents during a predefined period for the one or more metrics from one or more performance management databases; calculating a change in KPI; when the calculated change in KPI is negative: calculating an influence of each metric on the calculated change in KPI; calculating an influence of each agent on the calculated change in KPI; checking each agent having a negative change of a goal-accomplished-percentage to downgrade a contribution-factor of an agent having a top-negative-influence on the calculated change in KPI; and forwarding the downgraded contribution factor of the agent having the top-negative-influence on the calculated change in KPI to a downstream application.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for providing unsupervised model health monitoring extract from an interaction database, first and second random samples of interaction data relating to first and second sets of interactions during first and second periods of time; score each interaction of the first and second sets of interactions by applying a predictive model to the related interaction data to produce first and second sets of interaction scores; identify a plurality of sub-populations among the first and second sets of interaction scores by applying a clustering model to the first and second sets of interaction scores; measure distances between each of the plurality of sub-populations among the first and second sets of interaction scores; compare the distances of the first period of time and the distances of the second period of time; and generate an alert when the comparison exceeds a predefined threshold.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods of calculating a score for a chain of interactions in a call center, 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.

Abstract: A system and method for selecting a training program from a plurality of training programs for a selected worker may include evaluating performance improvement of a plurality of workers who have taken training programs of the plurality of training programs, and associating a performance improvement grade to each of the workers for each of the training programs the worker has taken; selecting, in a worker database, workers that are similar to the selected worker; and selecting the training program for the selected worker based on the performance improvement grades associated with the similar workers for the plurality of training programs.