Securus Technologies, Inc.

Charles Barrasso

Jay L Gainsboro

Lee A Weinstein

Ken Sinclair

Kenneth H Sinclair

Jonathan T Klein

George Roberts

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, which can provide for user enrollment of an account, are provided. The enrollment of an account can be performed in a controlled-environment facility using a system(s) that provides assurance that the user of an account was the person that enrolled the account. A method, which may be employed in various systems and/or facilities, includes, using an enrollment module operating on at least one processor, generating respective confidential codes associated with user accounts. Each of the user accounts has at least one of the confidential codes and is for a respective one user. The method also includes receiving, at the enrollment module, respective enrollment information of each of enrolling individuals. The respective enrollment information is to enroll respective ones of the user accounts. The method further includes, after the receiving, transmitting from the enrollment module the respective confidential codes associated with the respective ones of the user accounts.

Account enrollment systems and processes

In one aspect, the present invention facilitates the investigation of networks of criminals, by gathering associations between phone numbers, the names of persons reached at those phone numbers, and voice print data. In another aspect the invention automatically detects phone calls from a prison where the voiceprint of the person called matches the voiceprint of a past inmate. In another aspect the invention detects identity scams in prisons, by monitoring for known voice characteristics of likely imposters on phone calls made by prisoners. In another aspect, the invention automatically does speech-to-text conversion of phone numbers spoken within a predetermined time of detecting data indicative of a three-way call event while monitoring a phone call from a prison inmate. In another aspect, the invention automatically thwarts attempts of prison inmates to use re-dialing services. In another aspect, the invention automatically tags audio data retrieved from a database, by steganographically encoding into the audio data the identity of the official retrieving the audio data.

Multi-party conversation analyzer and logger

The multi-party conversation analyzer of the present invention allows users to search a database of recorded phone calls to find calls which fit user-defined criteria for 'suspicious calls'. Such criteria may include indications that a call included a 3-way call event, presence of an unauthorized voiced during the call, presence of the voice of an individual known to engage in identity theft, etc. A segment of speech within a call may be graphically selected and a search for calls with similar voices rapidly initiated. Searches across the database for specified voices are speeded by first searching for calls which contain speech from cohort speakers with similar voice characteristics.

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Franklin, MA, United States of America

Charles Barrasso