公开(公告)号：US20220207457A1

公开(公告)日：2022-06-30

申请号：US17136045

申请日：2020-12-29

Applicant: NICE LTD

Inventor： Harshit Kumar SHARMA ,  Salil DHAWAN ,  Rahul VYAS

IPC: G06Q10/06 ,  G06Q10/10 ,  G06Q30/00 ,  G06F16/22 ,  H04M3/523

Abstract: A computerized-method for gauging agent's self-assessment effectiveness, is provided herein. The computerized-method includes for each interaction (i) operating a Self-assessment Consolidation module to calculate a confidence-interval for each data-point of one or more preconfigured data-points, and (ii) operating a Self-assessment Divergence Determinant (SDD) module. The operating of the SDD includes: retrieving one or more data-points of the interaction; for each data-point retrieving the confidence interval; setting a divergence-indicator as zero, when the data point is within the confidence-interval; setting the divergence-indicator as a subtraction of the data point from the calculated lower-bound, when the data-point is lower than the lower-bound of the confidence-interval; and setting the divergence-indicator as a subtraction of the calculated upper-bound from the data-point, when the data-point is greater than the upper-bound of the confidence-interval. Then, accumulating the divergence-indicator of the data-points to yield an SDD for the interaction; and sending the SDD to one or more systems.

公开(公告)号：US20220300886A1

公开(公告)日：2022-09-22

申请号：US17833909

申请日：2022-06-07

Applicant: NICE LTD

Inventor： Salil DHAWAN ,  Harshit Kumar SHARMA ,  Rahul VYAS

IPC: G06Q10/06 ,  H04M3/22

Abstract: A computerized-method for calculating an After-Call-Work (ACW) factor of an interaction in a contact center, by which a related recording may be filtered for evaluation is provided herein. The method includes an After-Call-Work (ACW) factor calculation module. The operating of the ACW factor calculation module includes: (i) receiving agent recording of the interaction. (ii) aggregating data fields associated with: (a) the interaction; and (b) the customer; (iii) retrieving ACW time of the interaction; (iv) forwarding the aggregated data fields to a machine learning model; (v) operating the machine learning model to calculate a predicted ACW time, based on the aggregated data fields; (vi) calculating an ACW factor based on the received time of ACW and the calculated predicted ACW time; and (vii) sending the calculated ACW factor to a platform by which the platform is preconfigured to distribute the interaction for evaluation, based on the ACW factor.