公开(公告)号：US20240078559A1

公开(公告)日：2024-03-07

申请号：US17902495

申请日：2022-09-02

Applicant: Verint Americas Inc.

Inventor： Grant Anderson

IPC: G06Q30/00 ,  G06F40/186 ,  G06F40/279 ,  G06F40/40

CPC classification number: G06Q30/016 ,  G06F40/186 ,  G06F40/279 ,  G06F40/40

Abstract: The template generation system receives interaction data stored by the CEC from an interaction database and customer service templates (if any) from a template database. The template generation system processes interaction data and customer service templates to learn the domain language of CSR responses and the template responses within the CEC. The template generation system encodes the learned language and generates sentence vector embeddings for the CSR responses and template responses. Based on the learned language, the encoding, and the sentence vector embeddings, the template generation system processes CSR responses derived from the interaction data and customer service templates to predict the need for new customer service templates. Based on the predicted need for new customer service templates, the template generation system provides customer service template suggestions and may also auto-generate suggested customer service templates.

公开(公告)号：US20230230585A1

公开(公告)日：2023-07-20

申请号：US18096942

申请日：2023-01-13

Applicant: Verint Americas Inc.

Inventor： Grant Anderson ,  Scott Mackie ,  Neil Eades ,  Sean Robertson

IPC: G10L15/183 ,  G06N3/08 ,  G10L15/16 ,  G10L15/06 ,  G10L15/30

CPC classification number: G10L15/183 ,  G06N3/08 ,  G10L15/16 ,  G10L15/30 ,  G10L15/063 ,  G10L15/04

Abstract: A system for generating wrap-up information is capable of learning how interactions are transformed into contact notes and outcome codes using natural language processing and can generate the contact notes and outcome codes for new incoming interactions by applying prediction models trained on interaction data, contact notes and outcome codes. The system for generating wrap-up information receives interaction data, including interaction audio data, interaction transcripts, associated contact notes and associated outcome codes. The interaction transcripts are generated from the previous interactions between agents and customers. The contact notes and outcome codes are generated by agents during the associated previous interactions. The system processes and uses the interaction data to train prediction models to analyze interaction audio data and interaction transcripts and predict appropriate contact notes and outcome codes for the interaction. Once trained the prediction model(s) can generate appropriate contact notes and outcome codes for new interactions.

公开(公告)号：US20220277228A1

公开(公告)日：2022-09-01

申请号：US17683332

申请日：2022-02-28

Applicant: Verint Americas Inc.

Inventor： James Nies ,  Matthew Pyke ,  Paul Gorman ,  Ash Sood ,  Neil Eades ,  Grant Anderson ,  Alastair Grant

IPC: G06N20/00

Abstract: An artificial intelligence (AI) application uses an external machine learning component from a different computing environment to develop context data for use by the AI application. The context data includes raw data outputs from the external machine learning component. An active machine learning component is executed with the context data and provides a suggested next step to a computer to implement as an automated output. A feedback loop adds the suggested next step from the active machine learning component to the context data and forms an augmented data set for providing context to the AI application. A context component selects a rule from a rules engine that corresponds to the augmented data set. The computer implements an automated output according to the rule that was selected.