Abstract: An alert generator in a communication system for processing a call includes at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configure to operate on the call. Heuristic logic is configured to transform the call classifiers into a plurality of weighted sub-metrics for the call, and aggregate normalized Gaussian logic is configured to transform the weighted sub-metrics into a metric control. A threshold analyzer is configured to generate an alert signal to the communication system based on the metric control meeting a condition.

Abstract: A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow.

Abstract: A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow.

Abstract: An alert generator in a communication system for processing a call includes at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configure to operate on the call. Heuristic logic is configured to transform the call classifiers into a plurality of weighted sub-metrics for the call, and aggregate normalized Gaussian logic is configured to transform the weighted sub-metrics into a metric control. A threshold analyzer is configured to generate an alert signal to the communication system based on the metric control meeting a condition.

Abstract: A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow. The system includes a scorecard interface operable to select a target and an indication of the metric control to apply for the target, and to apply the metric control to generate and display a historical performance visualization and a performance feed of the metric for the target.

Abstract: A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow. The system includes a scorecard interface operable to select a target and an indication of the metric control to apply for the target, and to apply the metric control to generate and display a historical performance visualization and a performance feed of the metric for the target.

Abstract: A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow.

Abstract: A call flow manager includes a graph of connected nodes implementing a call center routing flow, and corresponding views for examining and modifying the nodes and graph. The call flow manager implements a set of fundamental node types, with complexity and behavior encapsulated within the nodes. The nodes process calls and add tags to the calls that may be utilized in downstream nodes of the call flow.

Abstract: An alert generator in a communication system for processing a call includes at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configure to operate on the call. Heuristic logic is configured to transform the call classifiers into a plurality of weighted sub-metrics for the call, and aggregate normalized Gaussian logic is configured to transform the weighted sub-metrics into a metric control. A threshold analyzer is configured to generate an alert signal to the communication system based on the metric control meeting a condition.

Abstract: A method whereby an audio content is analyzed for speech semantic content and speech vocal content to determine audio content metrics. Additionally, the method may also incorporate the similarity of the audio content to other audio contents and weight the analyses to determine audio content metrics.

Abstract: A distributed multisensory system to record spatially diverse events configures a group of sensors searching sensed events utilizing random access search. The system utilizes presence and co-location detection to identify objects or people in an area. The system then processes audio signals utilizing a multimodal network and machine learning tiered with obfuscated text indexing to provide high-speed, high accuracy searching of stored data. In addition, the obfuscated text allows the system to not have to store an audio transcript, increasing the security of the system. The system utilizes beamforming to merge resultant audio streams for the recorded event.