Abstract: Systems and methods for providing interaction recordings with removed personally identifiable information (PII) are disclosed, the systems and methods involving: extracting, from an interaction recording, using an automatic speech recognition engine, a timestamped recording transcript; identifying, using an artificial intelligence (AI) engine, time periods of the timestamped recording transcript which disclose PII; and removing, from the interaction recording, data present during the time periods which disclose PII, to produce a secure interaction recording.

Abstract: Systems and methods for evaluating and determining a root cause of a change in performance in a workforce management system are provided. Graphs can be created based on key performance indicators, behaviors and metrics to determined a root cause for agents who have a decrease in performance. The root cause can be transmitted to other computing systems which can select which interactions to record, selects a coaching program to run, or any combination.

Abstract: A computerized-system for converting a time-off request of an agent to a shift-trade with another agent or to a self-swap schedule, in a contact center is provided here in. Upon receiving a time-off request from a source-agent, operating a Time-off Convertor (TC) module. The TC module includes (i) searching open trade requests in dates where the source-agent doesn't have a scheduled-shift, to yield a list-of-trading-agents; (ii) retrieving from the yielded list-of-trading-agents, compatible-agents who do not have a scheduled-shift in the specified date; (iii) presenting the compatible-agents, via a display unit; (iv) enabling the source-agent a selection of an agent from the presented compatible-agents and upon selection of the agent by the source-agent, sending an approval request to the agent; and (v) upon acceptance of the agent, sending to the source-agent and to the agent a trade-success-notification, and operating change of scheduled shifts in one or more WFM MSs.

Abstract: A system and method for managing interaction distribution, for example in a contact center, may, for a digital skill relating to interaction handling and impacted by a lack of resources during an interval, determine an agent having the digital skill; raise the identified agent's concurrency setting for the digital skill for the interval; and distribute interactions according to the concurrency setting. The concurrency setting may determine how many interactions relating to the digital skill the agent can handle concurrently.

Abstract: A system and method for intelligent computerized task scheduling and execution, including: optimizing a time off rule including a quota of time off units for a time period—by changing the quota of time off units based on calculating a time off utilization indicator; updating a computerized task schedule based on the optimized time off rule; and executing tasks based on the updated schedule. In some embodiments, time off optimization may include identifying, by a machine learning model (such as, e.g., a generative artificial intelligence or large language model), rules matching a given time off rule, and deleting/merging rules based on similar rule names or activity codes. The machine learning model may generate rule names for merged rules. Optimized time off rules may be used to accept or reject time off requests transmitted and/or received, e.g. over a data or communication network.

Abstract: A system and method for automatically identifying and masking information, including: identifying, using a machine learning model, one or more tokens in an image file using one or more data items extracted from the image file and including one or more strings, and masking one or more of the identified tokens in the image file. In some embodiments, masking includes coloring locations in the image file based on extracted location information—describing boxes or polygons bounding or surrounding of the strings in the image file. Tokens identified and/or masked by some embodiments may include personally identifiable information (PII) or data, and some embodiments may determine the type of PII data for tokens or texts identified as including PII. In some embodiments, rows of text extracted from the image file may be provided to the machine learning model in a sequential manner (e.g., one row after another).

Abstract: A computerized-method for detecting market sentiment manipulation that is related to financial-instruments trading. The computerized-method includes: (i) monitoring incoming alerts in an analytics-engine. (ii) retrieving data from each alert, by operating a context-extraction module; (iii) for each alert: a. collecting feeds from social-media servers based on the retrieved data by operating a social-media feeds-extraction module; b. for each feed, generating a summary by using Gen AI; and c. analyzing a social-media sentiment by providing the generated summary of each feed to the Gen-AI; (iv) for each financial instrument that has been traded in the preconfigured period, searching an anomaly between a sentiment from traditional news source and the analyzed social-media sentiment; (v) storing each anomaly in an anomalies database; and (vi) pausing each financial-transaction that has been processed and related to the financial-instrument in the anomalies database.

Abstract: A device, system and method for accelerated compliance testing for real-time detection of suspicious transactions in a transaction stream. A machine learning model may determine a global trend of cumulative transaction behavior based on all, a majority or a representative subset, of the stream of transactions. For each transaction in the stream, a skip rule engine may sort each security compliance test to be skipped or not skipped based on the test's relevance to the global trend of the cumulative transaction behavior. The not skipped security compliance tests may be executed in real-time for each transaction in the stream to generate real-time partial security assessments therefore comprising real-time suspicious transaction alerts. The remaining tests may be skipped in real-time and executed, at a time delay after transaction times, to generate time delayed supplemental security assessments therefore comprising time delayed suspicious transaction alerts to complete all compliance testing.

Abstract: Stress detection systems and methods, and non-transitory computer readable media, include building a library including previously identified stressful sentences and stressful phrases; receiving, by a trained neural network model, the library; receiving, by the trained neural network model, a text interaction between a customer and an agent; calculating, by the trained neural network model, a cosine similarity score between each stressful sentence or stressful phrase in the library and each sentence in the text interaction; determining, by the trained neural network model, a probability that the text interaction is stressful based on the calculated cosine similarity score; determining that a percentage of stressful interactions for the agent in a time interval is greater than a threshold percentage; providing a manager with recommended actions to decrease stress on the agent; receiving, from the manager, a selection of one or more recommended actions; and implementing the one or more recommended actions.

Abstract: A system and method are provided for evaluating data items using a machine learning model, including: producing, by a machine learning model, answers to questions applied to an input data item, where the questions and data item are input to the machine learning model; calculating a score for the input data item based on the produced answers; and transmitting one or more output data items to a remote computer over a communication network based on the calculated score. Some nonlimiting embodiments of the invention may relate to analyzing text data such as interaction transcripts in a contact center environment. In some embodiments questions may be included in a form and/or evaluation plan, and questions may include critical questions which are to be answered in the data item is to be assigned a non-zero scores. Questions may be organized in levels, and scores may be calculated based on the levels.

Abstract: Systems and methods for monitoring a quality of interactions include: receiving one or more quality metrics relating to one or more quality targets; determining, on a periodic basis, a current quality value for each of the one or more quality targets; calculating a deviation of the current quality value from a desired value of the quality target; weighting the one or more quality targets according to the calculated deviation; and determining a set of one or more interactions for review based on one or more quality metric deviations and the weighted quality targets.

Abstract: A method and system for building and implementing a meta-machine learning (meta-ML) optimization engine for a neural network (NN) or a machine learning (ML) connective model. A computer processor may iteratively simulate a backpropagation algorithm by executing a sequence of optimization steps. At each optimization step a position of a loss function may be determined that may be closer than a previously determined position of the loss function to a local minimum. A computer processor may compute and store after each iteration a detachment of the loss function, learning rate, and optimal learning rate. A computer processor may train a machine learning connective model to model the optimal learning rates of the simulated backpropagation algorithm. The meta-ML optimization engine may be implemented for a NN or ML connective model by generating a modified backpropagation algorithm in which algorithmic features of gradient descent may be replaced by the meta-ML optimization engine.

Abstract: A device, system and method for accelerated compliance testing for real-time detection of suspicious transactions in a transaction stream. A machine learning model may determine a global trend of cumulative transaction behavior based on all, a majority or a representative subset, of the stream of transactions. For each transaction in the stream, a skip rule engine may sort each security compliance test to be skipped or not skipped based on the test's relevance to the global trend of the cumulative transaction behavior. The not skipped security compliance tests may be executed in real-time for each transaction in the stream to generate real-time partial security assessments therefore comprising real-time suspicious transaction alerts. The remaining tests may be skipped in real-time and executed, at a time delay after transaction times, to generate time delayed supplemental security assessments therefore comprising time delayed suspicious transaction alerts to complete all compliance testing.

Abstract: Systems and methods for monitoring a performance of a chat bot include: analysing, by an artificial intelligence (AI) module, a transcript of at least one conversation in which the chat bot participates; identifying, by the AI module, one or more skills of the chat bot where performance falls below a pre-defined performance threshold; determining a chat bot effectiveness score; and determining whether to: automatically update a set of predefined responses of the chat bot based on at least one of: the one or more identified skills; and the chat bot effectiveness score; else, outputting an indication of the one or more identified skills and the chat bot effectiveness score.

Abstract: A computerized-method for identifying a contact-reason of a business-process that has been discovered by a desktop-analytics-tool. The computerized-method includes: (i) retrieving a random-sample of a preconfigured number of instances of a discovered-routine from the routines-datastore; (ii) for each instance in the random-sample of the preconfigured number of instances matching a related transcript-segment; (iii) identifying a contact-reason of each instance by operating a first-GEN-AI with LLM with a first-prompt-text including an embedded related transcript-segment; The first-GEN-AI with LLM has been trained to provide the contact-reason based on the transcript-segment embedded in the first-prompt-text, and (iv) identifying the contact-reason of the discovered-routine as the contact-reason of the business process by operating a second-GEN-AI with LLM with a second-prompt-text for aggregation of all the contact-reasons of all instances in the random-sample.

Abstract: A computer-implemented method for playing a personalized voice recording with a status update to follow-up customer on an inbound call in a contact center. The computer-implemented method includes marking one or more open tickets as predicted for follow-up inbound call and predicted date and time for the follow-up call in an inbound database; updating status of one or more open ticket marked as predicted for follow-up inbound call and predicted date and time; converting the updated status to a voice recording; and operating an inbound software to identify an inbound call as a follow-up inbound call of a customer received via a Voice over Internet Protocol (VoIP) network communicating with a customer's mobile device. The customer has an open ticket with an updated status and playing voice recording with the status update in the identified inbound call, thus reducing customers waiting time and agents' workload.

Abstract: A computerized-method for training and applying a Machine Learning (ML) textual behavioral-identification-model to authenticate an agent, in a digital multi-channel environment, is provided herein. The computerized-method may include: (i) training a ML-textual-behavioral-identification-model using retrieved textual responses of each agent in one or more historical-interactions which were conducted in a controlled environment, as a training dataset.

Abstract: An event prioritization system and methods are provided that are configured to dynamically compute call complexity for voice data calls using topics identified from call transcripts. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform call evaluation operations which include receiving a transcript of a voice data call, parsing the transcript for at least one topic, generating, based on the parsing, a list of topics, analyzing, by a call complexity evaluation engine, the list of topics for a complexity rating, determining, based on the complexity rating and a length of the voice data call, a deviation from an expectation, flagging the voice data call, and outputting the flagged voice data call via an interface.

Abstract: A system and method may identify computer-based processes which may be candidates for automation. Embodiments may involve a semi-supervised approach for identifying processes as automation opportunities. Transition probabilities for pairs of routines within a candidate process may be calculated based on a set of instances of the process (e.g., in a dataset of computer actions) using a dynamic time-window optimization procedure, where transition times may be measured for a plurality of instances of a first and second routines of a given pair of routines, and where statistical distributions may be calculated and used for deriving one or more time windows, describing a predetermined percentile (e.g., the 70th percentile) of the measured transitions and used for estimating a transition probability for the pair of routines. In some embodiments, the input set of transitions and routines may be generated by a user or business analyst using a graphical user interface (GUI).

Abstract: A device, system and method is provided for monitoring a user's interactions with Internet-based programs or documents. Content may be extracted from Internet server traffic according to predefined rules. Extracted content may be associated with a user's Internet interaction. The user's Internet interaction may be stored and indexed. The user's Internet interaction may be analyzed to generate a recommendation provided to a contact center agent while the contact center agent is communicating with said user for guiding the user's interaction, for example, in real-time. Traffic other than Internet server traffic may also be used.