公开(公告)号：US11790302B2

公开(公告)日：2023-10-17

申请号：US16715136

申请日：2019-12-16

Applicant: NICE LTD.

Inventor： Lior Ben Eliezer ,  Hila Kneller ,  Gennadi Lembersky

IPC: G06Q10/0639 ,  G06N20/00 ,  G06N5/04

CPC classification number: G06Q10/06393 ,  G06N5/04 ,  G06N20/00

Abstract: Calculating a score for a chain of interactions in a call center may include: during a first training phase, train a first model which, given an interaction and interaction metadata, predict an initial estimated customer satisfaction score; during a second training phase, train a second model which, given an interaction and interaction metadata, text and metadata of an immediately preceding interaction in a chain of interactions, and features of the chain, predict a refined estimated customer satisfaction score; and during an inference phase: given a chain of interactions and metadata of each interaction, compute an initial estimated customer satisfaction score for each interaction using the first model; beginning with a second interaction in the chain and metadata of each interaction, compute a refined estimated customer satisfaction score for each interaction using the second model; combine the interaction scores into a combined customer satisfaction score; and output the score.

公开(公告)号：US11676067B2

公开(公告)日：2023-06-13

申请号：US16791316

申请日：2020-02-14

Applicant: Nice Ltd.

Inventor： Hila Kneller ,  Lior Ben Eliezer ,  Yuval Shachaf ,  Gennadi Lembersky ,  Natan Katz

IPC: G06N20/00 ,  H04L51/02 ,  G10L15/22 ,  G10L15/18 ,  G10L15/16 ,  G10L15/06 ,  G10L15/183 ,  G06F40/30 ,  G10L13/00

CPC classification number: G06N20/00 ,  G06F40/30 ,  G10L15/063 ,  G10L15/16 ,  G10L15/183 ,  G10L15/1815 ,  G10L15/22 ,  H04L51/02 ,  G10L13/00 ,  G10L2015/0631 ,  G10L2015/0633 ,  G10L2015/223

Abstract: A system and method for creating input data to be used to train a conversational bot may include receiving a set of conversations, each conversation including sentences, classifying each sentence into a dialog act taken from a number of dialog acts, for each set of sentences classified into a dialog act, clustering the set of sentences into clusters based on the content (e.g. text) of the sentences, each cluster having a cluster name or label, and generating a language model based on the cluster labels. Slots may be identified in the sentences based in part on the dialog act classifications. A bot may be trained using data such as the slots, language model, and clusters.