公开(公告)号：US20220366197A1

公开(公告)日：2022-11-17

申请号：US17318467

申请日：2021-05-12

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： ARNON MAZZA ,  LEV HAIKIN ,  EYAL ORBACH ,  AVRAHAM FAIZAKOF

IPC: G06K9/62 ,  G06N20/20

Abstract: A method and system for finetuning automated sentiment classification by at least one processor may include: receiving a first machine learning (ML) model M0, pretrained to perform automated sentiment classification of utterances, based on a first annotated training dataset; associating one or more instances of model M0 to one or more corresponding sites; and for one or more (e.g., each) ML model M0 instance and/or site: receiving at least one utterance via the corresponding site; obtaining at least one data element of annotated feedback, corresponding to the at least one utterance; retraining the ML model M0, to produce a second ML model Mi, based on a second annotated training dataset, wherein the second annotated training dataset may include the first annotated training dataset and the at least one annotated feedback data element; and using the second ML model Mi, to classify utterances according to one or more sentiment classes.

公开(公告)号：US20250165720A1

公开(公告)日：2025-05-22

申请号：US18386335

申请日：2023-11-02

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： LEV HAIKIN ,  AVRAHAM FAIZAKOF ,  NELLY DAVID ,  EYAL ORBACH ,  ROTEM MAOZ

IPC: G06F40/40 ,  G06F16/951 ,  G06F40/279 ,  G06F40/35 ,  G06Q30/015

Abstract: A method in a contact center for generating insights from conversation data derived from interactions and storing the insights in an index. The method may include: determining an insight type; based on the insight type, determining inputs including a question prompt, answer prefix, and relevant portion of the conversation data; inputting the inputs into a LLM configured to receive the inputs and generate output text answering a question contained in the question prompt pursuant to an answer form suggested by the answer prefix given content contained in the relevant portion of the conversation data; generating the output text via operation of the LLM; transforming the output text of the first insight via a sentence transformer into vector embedding representative of a semantic meaning of the output text; and storing the computed vector embedding of the first insight in the index.

公开(公告)号：US20230144379A1

公开(公告)日：2023-05-11

申请号：US17520816

申请日：2021-11-08

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： LEV HAIKIN ,  ARNON MAZZA ,  EYAL ORBACH ,  AVRAHAM FAIZAKOF

IPC: G10L15/197 ,  G10L15/06 ,  G10L15/22 ,  G10L15/10 ,  G06N20/00

CPC classification number: G10L15/197 ,  G10L15/063 ,  G10L15/22 ,  G10L15/10 ,  G06N20/00 ,  G10L2015/0635

Abstract: A system and method of automatically discovering unigrams in a speech data element may include receiving a language model that includes a plurality of n-grams, where each n-gram includes one or more unigrams; applying an acoustic machine-learning (ML) model on one or more speech data elements to obtain a character distribution function; applying a greedy decoder on the character distribution function, to predict an initial corpus of unigrams; filtering out one or more unigrams of the initial corpus to obtain a corpus of candidate unigrams, where the candidate unigrams are not included in the language model; analyzing the one or more first speech data elements, to extract at least one n-gram that comprises a candidate unigram; and updating the language model to include the extracted at least one n-gram.

公开(公告)号：US20220382982A1

公开(公告)日：2022-12-01

申请号：US17318524

申请日：2021-05-12

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： EYAL ORBACH ,  AVRAHAM FAIZAKOF ,  ARNON MAZZA ,  LEV HAIKIN

IPC: G06F40/289 ,  G06F16/2458 ,  G06F16/248

Abstract: A method and system for automatic topic detection in text may include receiving a text document of a corpus of documents and extracting one or more phrases from the document, based on one or more syntactic patterns. For each phrase, embodiments of the invention may: apply a word embedding neural network on one or more words of the phrase, to obtain one or more respective word embedding vectors; calculate a weighted phrase embedding vector, and compute a phrase saliency score, based on the weighted phrase embedding vector. Embodiments of the invention may subsequently produce one or more topic labels, representing one or more respective topics in the document, based on the computed phrase saliency scores, and may select one or more topic labels according to their relevance to the business domain of the corpus.