US 11,790,177 B1
Communication classification and escalation using machine learning model
Charles Barrasso, Franklin, MA (US); Kenneth H. Sinclair, Newton, MA (US); and Jeremy Moody, Waban, MA (US)
Assigned to Securus Technologies, LLC, Carrollton, TX (US)
Filed by Securus Technologies, LLC, Carrollton, TX (US)
Filed on Dec. 18, 2020, as Appl. No. 17/127,031.
Int. Cl. G06F 40/30 (2020.01); G06F 16/248 (2019.01); G06N 20/00 (2019.01); G06Q 50/26 (2012.01); G06F 40/279 (2020.01); H04M 3/22 (2006.01); G06V 20/40 (2022.01); G10L 15/26 (2006.01); G10L 15/22 (2006.01)
CPC G06F 40/30 (2020.01) [G06F 16/248 (2019.01); G06F 40/279 (2020.01); G06N 20/00 (2019.01); G06Q 50/26 (2013.01); G06V 20/41 (2022.01); H04M 3/2281 (2013.01); G06V 20/44 (2022.01); G10L 15/22 (2013.01); G10L 15/26 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
obtaining metadata for a communication;
obtaining features of the content in the communication;
determining from the metadata and or a communication transcript, using a machine learning model, a likelihood that the communication comprises suspicious content or a topic of interest; and
providing one or more communication summaries as an input to the machine learning model, wherein the model is configured to process the communications in accordance with current values of a set of model parameters to generate as output a proposed likelihood that the communication comprises suspicious content or topic of interest.