Abstract: A computerized-method for a personalized screen recording in a contact center is provided herein. The computerized-method includes, before each interaction between an agent and a customer, operating a predictive screen recording module to yield an Agent Recording Percentage (ARP) value; and based on the ARP value, operating a recording of screen events module for recording one or more voice or digital interactions, on one or more screens associated to a computing device of the agent.

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 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: A method and system for dynamically determining a minimum support for automation mining is provided. The method and system include modifying the minimum support pattern such that the minimum support can result pattern mining algorithms finding a sufficient number of patterns in a practical duration.

Abstract: A method for allocating resources for a plurality of time intervals, including: receiving a forecasted workload and at least one required service metric value; applying a search algorithm to identify an initial allocation assignment; inputting the assignment to a machine learning algorithm, the machine learning algorithm trained on historic data of past intervals; predicting an expected service metric value provided by the initial allocation assignment; adjusting the initial allocation assignment based on a difference between the expected service metric value and the corresponding required service metric value; iteratively repeating the applying, inputting, predicting, and adjusting operations until one of: the expected service metric value predicted for an adjusted allocation assignment is within a predetermined distance of the corresponding at least one required service metric value for the interval; or a predetermined time has elapsed.

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 method and system for analyzing and connecting computer-based actions into sentences may include for a series of computer-based actions, determining the case ID for the action for each action where an identifier or case ID can be determined, creating sequences of subsets of the series of computer-based actions using the case ID, and merging sequences having computer-based actions having the same case ID. A set of case IDs may be extracted from the actions using a clustering algorithm based on features of potential case IDs such as gaps in appearance of potential case IDs in a sequence of actions and consecutive appearances of potential case IDs in a sequence of actions. The extracted case IDs may be used when creating sequences.

Abstract: A system is provided to categorize voice prints during a voice authentication. The system includes a processor and a computer readable medium operably coupled thereto, to perform voice authentication operations which include receiving an enrollment of a user in the biometric authentication system, requesting a first voice print comprising a sample of a voice of the user, receiving the first voice print of the user during the enrollment, accessing a plurality of categorizations of the voice prints for the voice authentication, wherein each of the plurality of categorizations comprises a portion of the voice prints based on a plurality of similarity scores of distinct voice prints in the portion to a plurality of other voice prints, determining, using a hidden layer of a neural network, one of the plurality of categorizations for the first voice print, and encoding the first voice print with the one of the plurality of categorizations.

Abstract: A system and method for accessing elements of a table in a digital image of the table, including: obtaining the digital image of the table; finding table elements in the digital image based on digital table properties, wherein the table elements define table cells; calculating coordinates of the table cells in the digital image based on the table elements; and accessing content of a selected table cell in the digital image using the coordinates of the selected table element.

Abstract: A system and method analyzes computer actions to identify computer-based processes (e.g. computer-user interactions) which are automation candidates. A data gathering process executed by a processor on a computer may collect low-level user action information or items, each low-level user action information or item including for example an input type description, a user name, and screen window information. At a computer sequential pattern mining may be applied to determine a set of subprocesses, each subprocess including a series of low-level user actions, each user action associated with a user action vector, and each subprocess associated with a subprocess vector generated from user action vectors associated with (typically generalized) low-level user actions comprised in the subprocess. The subprocess vectors may be grouped or clustered to create processes. For each process, an automation score may be calculated using the actions in the subprocesses in the process.

Abstract: Systems and methods for generating and implementing a real-time multi-factor authentication policy across multiple channels, are configured to: during a pre-authentication stage: receive, via a user interface, information defining one or more scenarios; receive, via the user interface, information defining one or more authentication flows; for each of the one or more scenarios, map one of the one or more authentication flows to a given scenario; and generate a multi-factor authentication policy associated with each of the one or more scenarios; and during a real-time authentication stage: upon receiving an interaction, identify, by a decision engine, a relevant scenario of the one or more scenarios; implement, by the decision engine, the multi-factor authentication policy associated with the relevant scenario; and determine, by the decision engine, an authentication result.

Abstract: A computerized-method for calculating an agent skill-satisfaction-index and utilization thereof, is provided herein. The computerized-method includes operating an Agent-Skill-Satisfaction-Index (ASSI)-scoring module.

Abstract: A method of assisting an agent in real-time includes receiving a call interaction between a customer and an agent; identifying words spoken in the call interaction; providing the words to a behavioral models module; computing a score for a plurality of behavioral metrics; providing a phrase formed by the words to a knowledge article selection module; providing each score for the plurality of behavioral metrics to the knowledge article selection module; providing a plurality of knowledge selection rules to the knowledge article selection module; evaluating a combination of the phrase and the scores of the plurality of behavioral metrics against each of the plurality of knowledge selection rules; matching a knowledge selection rule to the combination; selecting a knowledge article associated with the matched knowledge selection rule; generating a visual representation of the selected knowledge article; and presenting in real-time the visual representation on a graphical user interface.

Abstract: A system and method may identify a fraud ring based on call or interaction data by analyzing by a computer processor interaction data including audio recordings to identify clusters of interactions which are suspected of involving fraud each cluster including the same speaker; analyzing by the computer processor the clusters, in combination with metadata associated with the interaction data, to identify fraud rings, each fraud ring describing a plurality of different speakers, each fraud ring defined by a set of speakers and a set of metadata corresponding to interactions including that speaker; and for each fraud ring, creating a relevance value defining the relative relevance of the fraud ring.

Abstract: A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

Abstract: Methods for voice authentication include receiving a plurality of mono telephonic interactions between customers and agents; creating a mapping of the plurality of mono telephonic interactions that illustrates which agent interacted with which customer in each of the interactions; determining how many agents each customer interacted with; identifying one or more customers an agent has interacted with that have the fewest interactions with other agents; and selecting a predetermined number of interactions of the agent with each of the identified customers. In some embodiments, the methods further include creating a voice print from first and second speaker components of each interaction; comparing the voice prints of a first selected interaction to the voice prints from a second selected interaction; calculating a similarity score between the voice prints; aggregating scores; and identifying the voice prints that are associated with the agent.

Abstract: Systems and methods are provided for solving workforce management scheduling optimization decomposing and iteratively. Execution of a master problem can occur to select a best schedule among generated schedules for each employee of a group of employees while enforcing one or more global constraints. The generated schedule can be determined by execution of one or more sub-problems, each of the one or more sub-problems can be enforced work rules for the respective employee through the use of reduced cost for the employee. A flexible objective function can be executed to account for workforce management schedule wide metrics, including fitness of schedule to demand, fairness among the group of employees, schedule preferences and others.

Abstract: A system and method for real-time fraud detection with a social engineering phoneme (SEP) watchlist of phoneme sequences may perform real-time fraud prevention operations including receiving incoming call interactions and grouping the call interactions into one or more clusters, each cluster associated with a speaker's voice based on voiceprints. For a pair of voiceprints in a cluster, a phoneme sequence is extracted for each voice print. From the extracted phoneme sequences, a similarity score is then calculated to determine if a match exists between the extracted phoneme sequences based on a threshold. If determined a match exists, the phoneme sequence may be added to a SEP watchlist.

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 computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.