US 11,817,086 B2
Machine learning used to detect alignment and misalignment in conversation
Evgeniy Bart, Santa Clara, CA (US); and Margaret H. Szymanski, Santa Clara, CA (US)
Assigned to XEROX CORPORATION, Norwalk, CT (US)
Filed by Palo Alto Research Center Incorporated, Palo Alto, CA (US)
Filed on Mar. 13, 2020, as Appl. No. 16/817,944.
Prior Publication US 2021/0287664 A1, Sep. 16, 2021
Int. Cl. G06N 3/08 (2023.01); G10L 15/14 (2006.01); G06N 20/00 (2019.01); G10L 15/06 (2013.01); G06F 16/28 (2019.01); G10L 17/26 (2013.01)
CPC G10L 15/144 (2013.01) [G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06F 16/285 (2019.01); G06F 2203/011 (2013.01); G10L 17/26 (2013.01); G10L 2015/0631 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A method comprising:
receiving digitized media that represents a conversation between individuals;
extracting cues from the digitized media that indicate properties of the conversation;
entering the cues as training data into a machine learning module to create a trained machine learning model that detects misalignments in subsequent conversations between individuals, wherein misalignment is a lack of agreement and mutual understanding between the individuals of content and context of the content of the subsequent conversations; and
using the trained machine learning model in a processor to detect other misalignments in subsequent digitized conversations;
wherein the trained machine learning model detects misalignments in subsequent digitized conversations between individuals by determining a probability of each cue occurring in the subsequent digitized conversations under a normalcy model.