公开(公告)号：US12277395B2

公开(公告)日：2025-04-15

申请号：US18156050

申请日：2023-01-18

Applicant: NICE LTD.

Inventor： Lior Intrator ,  Dalya Bell ,  Tal Haguel ,  Neta Rosenfeld ,  Yonatan Rosen ,  Koren Gershoni

IPC: G06F40/30 ,  G06Q50/00 ,  G06V30/14 ,  G06V30/262

Abstract: A machine learning (ML) system and methods are provided that are configured to correlate text data with corresponding image data for image sentiment analysis. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform image processing operations which include receiving image data for an image posted on a social networking platform, determining whether there is text data, performing image data extraction operations, analyzing the text data, determining and combining a score for the image and text data, determining an image sentiment or a text sentiment, calculating weighted metrics based on the image sentiment or the text sentiment, determining historical customer data interactions of the customer, and recommending one or more actions based on the weighted metrics.

公开(公告)号：US11978442B2

公开(公告)日：2024-05-07

申请号：US17570121

申请日：2022-01-06

Applicant: NICE LTD

Inventor： Gennadi Lembersky ,  Neta Rosenfeld

IPC: G10L15/00 ,  G10L15/02 ,  G10L15/04 ,  G10L15/20 ,  G10L15/22 ,  G10L25/78 ,  H04M3/51

CPC classification number: G10L15/20 ,  G10L15/02 ,  G10L15/04 ,  G10L15/22 ,  G10L25/78 ,  H04M3/5175

Abstract: A system and methods are provided to analyze audio signals from an incoming voice call. The system includes a processor and a computer readable medium operably coupled thereto, to perform voice analysis operations which include receiving a first audio signal comprising a first audio waveform of a first speech between at least two users during the incoming voice call, accessing speech segment parameters for analyzing the audio signals, determining one or more talk-over segments in the first audio waveform using the speech segment parameters, extracting audio features from each of the one or more talk-over segments, determining, using a machine learning (ML) model trained for interruption analysis of the audio signals, whether each of the one or more talk-over segments are a negative interruption or a non-negative interruption based on the audio features, and determining whether to output a first notification for the negative interruption or the non-negative interruption.