Securus Technologies, Inc.

Kenneth H Sinclair

Charles Barrasso

Jeremy Moody

Systems and methods are disclosed for communication classification and escalation using a machine learning model. Metadata for a communication is obtained along with features of the content in the communication. Using a machine learning model, a likelihood that the communication comprises suspicious content or a topic of interest is determined from the metadata and or a communication transcript. If the likelihood that the communication comprises suspicious content or a topic of interest is at or above a predetermined threshold, then the communication is marked for review by a human agent; otherwise, the communication is ignored. A communication may have suspicious content or a topic of interest based upon the presence of keywords in the communication transcript either alone or in combined with metadata or other features of the content in the communication. The one or more keywords may be associated with illegal or unauthorized items, activities, or behaviors.

Communication classification and escalation using machine learning model

Systems and methods are described relating to detection of audio of voice communications from different reference points containing an identical conversation or portion of a conversation. A method can use an analytics module operating on at least one processor. Call signatures of a plurality of audio of voice communications are received. An index loaded with the call signatures is created, where segments of the call signatures are associated with buckets of the index. A first subset of one or more of the buckets associated with a number of the segments that exceeds a threshold is identified. An identical conversation portion in two or more of the plurality of audio of voice communications is identified based on a second subset of one or more of the buckets, where the first subset of one or more of the buckets is disregarded.

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Newton, MA, United States of America

Kenneth H Sinclair