US 11,704,477 B2
System and method of highlighting influential samples in sequential analysis
Ian Roy Beaver, Spokane, WA (US); Cynthia Freeman, Spokane Valley, WA (US); Jonathan Patrick Merriman, Spokane, WA (US); and Abhinav Aggarwal, Albuquerque, NM (US)
Assigned to Verint Americas Inc., Alpharetta, GA (US)
Filed by Verint Americas Inc., Alpharetta, GA (US)
Filed on Jun. 28, 2021, as Appl. No. 17/360,718.
Application 17/360,718 is a continuation of application No. 16/283,135, filed on Feb. 22, 2019, granted, now 11,048,854.
Claims priority of provisional application 62/633,827, filed on Feb. 22, 2018.
Prior Publication US 2022/0019725 A1, Jan. 20, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 17/00 (2019.01); G06F 40/117 (2020.01); G06F 40/35 (2020.01)
CPC G06F 40/117 (2020.01) [G06F 40/35 (2020.01)] 13 Claims
OG exemplary drawing
 
1. A computerized method for highlighting relative importance of portions of a conversation displayed on a graphical user interface, comprising:
storing the conversation in computerized memory connected to a computer processor that is configured to display conversation text on a graphical user interface, wherein a display of the conversation illustrates conversation data according to respective conversation participants' turns in providing conversation input;
weighting respective turns of the conversation by providing the conversation input of the respective turns to a hierarchical attention network stored in the memory, wherein the hierarchical attention network uses the processor to calculate sequential long-short-term-memory cells (LSTM) in the memory;
using later LSTM cell data to update weighting values for prior LSTM cell data in a sequence of turns of the conversation input;
wherein weighting the respective turns comprises adding conversation input data from additional later turns of the conversation to new LSTM cells; and
wherein the hierarchical attention network uses the computer processor to calculate sequential long-short-term-memory cells (LSTM) in the memory when a prior weighting of turns in the conversation have had a degree of uniformity greater than a uniformity tolerance threshold.