Abstract: A method of determining operational advantages associated with prospective use by a contact center of a predictive routing model. The operational advantages may include quantifying an expected lift in a target metric. The method includes: receiving an operational dataset associated with a time period of operation for the contact center; providing the predictive routing model configured to predict a score for the target metric for a given agent for a given interaction based on agent characteristic data associated with the given agent and interaction data associated with the given interaction; and, using the received predictive routing model, operational dataset, and agent characteristic data, computing the expected lift in the target metric assuming use of the predictive routing model during the time period. The algorithm used to compute the lift in the target metric is based on individual agent occupancy levels.

Abstract: A method that includes the steps of: generating, via a training data process, training data samples from respective journey data samples, the journey data samples comprising a customer journey represented by a sequence of events, values associated with respective event attributes, and journey outcome; and training a machine learning model using the training data samples. The training data process includes generating a vector embedding for each of the events included within the journey data samples that captures the value for each of the event attributes by: dividing the list of event into low and high cardinality groups via a cardinality threshold; for the low cardinality groups, categorically encoding the values according to a total number of unique values appearing therein; for the high cardinality groups: clustering the values and categorically encoding the values according to the group it resides in.

Abstract: A method for operationally transitioning between a first and second model in a contact center. The method includes defining a transition window and a pre-transition window. The method includes, during the pre-transition window, using the first model to determine first outputs and, therefrom, determining first output probability distributions and using, in real-time, the first outputs operationally in relation to pre-transition customers.

Abstract: A system and method are presented relating to emotion-based action recommendations. The present invention may include a recommender engine that analyzes several pieces of information and using artificial intelligence derived models, provides a course of action for a contact center agent and the probability and estimations behind the recommendation as a justification. System inputs include cost of the item or service being troubleshooted, estimated costs for different resolutions, cost of each escalation level of the interaction, history of previous interactions, agent costs, history of the current interaction, acoustic events and sentiment or primary detected emotions of the customer's transcript. The recommended course of action, rationale, and authorization can be provided as output. The system may record whether the agent followed the recommendation to improve further recommendations.

Abstract: A system and method are presented relating to emotion-based action recommendations. The present invention may include a recommender engine that analyzes several pieces of information and using artificial intelligence derived models, provides a course of action for a contact center agent and the probability and estimations behind the recommendation as a justification. System inputs include cost of the item or service being troubleshooted, estimated costs for different resolutions, cost of each escalation level of the interaction, history of previous interactions, agent costs, history of the current interaction, acoustic events and sentiment or primary detected emotions of the customer's transcript. The recommended course of action, rationale, and authorization can be provided as output. The system may record whether the agent followed the recommendation to improve further recommendations.

Abstract: A system and method for engaging in an automated dialog with a user. A processor retrieves a preset dialog flow that includes various blocks directing the dialog with the user. The processor provides a prompt to the user based on a current block of the dialog flow, receives an action from the user in response to the prompt, and retrieves a classification/decision tree corresponding to the dialog flow. The classification tree has a plurality of nodes mapped to the blocks of the dialog flow. Each of the nodes represents a user intent. The processor computes a probability for each of the nodes based on the action from the user. A particular one of the nodes is then selected based on the computed probabilities. A target block of the dialog flow is further identified based on the selected node, and a response is output in response to the identified target block.

Abstract: A method for engaging in an automated dialog with a user that includes: retrieving a preset dialog flow that includes various blocks directing the dialog with the user; providing a prompt to the user based on a current block of the dialog flow; receiving an action from the user in response to the prompt; and retrieving a classification tree corresponding to the dialog flow. The classification tree has a plurality of nodes mapped to the blocks of the dialog flow representing user intents. The processor computes a probability for each of the nodes based on the action from the user. A particular one of the nodes is then selected based on the computed probabilities. A target block of the dialog flow is identified based on the selected node, and a response is output in response to the identified target block.

Abstract: A method for engaging in an automated dialog with a user that includes: retrieving a preset dialog flow that includes various blocks directing the dialog with the user; providing a prompt to the user based on a current block of the dialog flow; receiving an action from the user in response to the prompt; and retrieving a classification tree corresponding to the dialog flow. The classification tree has a plurality of nodes mapped to the blocks of the dialog flow representing user intents. The processor computes a probability for each of the nodes based on the action from the user. A particular one of the nodes is then selected based on the computed probabilities. A target block of the dialog flow is identified based on the selected node, and a response is output in response to the identified target block.

Abstract: A system and method for engaging in an automated dialog with a user. A processor retrieves a preset dialog flow that includes various blocks directing the dialog with the user. The processor provides a prompt to the user based on a current block of the dialog flow, receives an action from the user in response to the prompt, and retrieves a classification/decision tree corresponding to the dialog flow. The classification tree has a plurality of nodes mapped to the blocks of the dialog flow. Each of the nodes represents a user intent. The processor computes a probability for each of the nodes based on the action from the user. A particular one of the nodes is then selected based on the computed probabilities. A target block of the dialog flow is further identified based on the selected node, and a response is output in response to the identified target block.

Abstract: A system and method for engaging in an automated dialog with a user. A processor retrieves a preset dialog flow that includes various blocks directing the dialog with the user. The processor provides a prompt to the user based on a current block of the dialog flow, receives an action from the user in response to the prompt, and retrieves a classification/decision tree corresponding to the dialog flow. The classification tree has a plurality of nodes mapped to the blocks of the dialog flow. Each of the nodes represents a user intent. The processor computes a probability for each of the nodes based on the action from the user. A particular one of the nodes is then selected based on the computed probabilities. A target block of the dialog flow is further identified based on the selected node, and a response is output in response to the identified target block.

Abstract: A system and method for engaging in an automated dialog with a user. A processor retrieves a preset dialog flow that includes various blocks directing the dialog with the user. The processor provides a prompt to the user based on a current block of the dialog flow, receives an action from the user in response to the prompt, and retrieves a classification/decision tree corresponding to the dialog flow. The classification tree has a plurality of nodes mapped to the blocks of the dialog flow. Each of the nodes represents a user intent. The processor computes a probability for each of the nodes based on the action from the user. A particular one of the nodes is then selected based on the computed probabilities. A target block of the dialog flow is further identified based on the selected node, and a response is output in response to the identified target block.