公开(公告)号：US11842312B2

公开(公告)日：2023-12-12

申请号：US17745422

申请日：2022-05-16

Applicant: Verint Americas Inc.

Inventor： Joseph Wayne Dumoulin ,  Cynthia Freeman ,  James DelloStritto

IPC: G06Q10/00 ,  G06Q10/0635 ,  G06F17/18 ,  G06Q30/018 ,  H04M15/00

CPC classification number: G06Q10/0635 ,  G06F17/18 ,  G06Q30/0185 ,  H04M15/00 ,  H04M15/47

Abstract: Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.

公开(公告)号：US20220019725A1

公开(公告)日：2022-01-20

申请号：US17360718

申请日：2021-06-28

Applicant: Verint Americas Inc.

Inventor： Ian Roy Beaver ,  Cynthia Freeman ,  Jonathan Patrick Merriman ,  Abhinav Aggarwal

IPC: G06F40/117 ,  G06F40/35

Abstract: Attention weights in a hierarchical attention network indicate the relative importance of portions of a conversation between an individual at one terminal and a computer or a human agent at another terminal. Weighting the portions of the conversation after converting the conversation to a standard text format allows for a computer to graphically highlight, by color, font, or other indicator visible on a graphical user interface, which portions of a conversation led to an escalation of the interaction from an intelligent virtual assistant to a human customer service agent.

公开(公告)号：US11928634B2

公开(公告)日：2024-03-12

申请号：US17939632

申请日：2022-09-07

Applicant: Verint Americas Inc.

Inventor： Joseph Wayne Dumoulin ,  Cynthia Freeman ,  James DelloStritto

IPC: G06Q10/00 ,  G06F17/18 ,  G06Q10/0635 ,  G06Q30/018 ,  H04M15/00

CPC classification number: G06Q10/0635 ,  G06F17/18 ,  G06Q30/0185 ,  H04M15/00 ,  H04M15/47

Abstract: Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.

公开(公告)号：US11868732B2

公开(公告)日：2024-01-09

申请号：US17883064

申请日：2022-08-08

Applicant: Verint Americas Inc.

Inventor： Cynthia Freeman ,  Ian Beaver

IPC: G06F40/30 ,  H04L51/02 ,  G06F40/35 ,  G10L15/22 ,  G06F40/117

CPC classification number: G06F40/30 ,  G06F40/117 ,  G06F40/35 ,  H04L51/02 ,  G10L15/22 ,  G10L2015/223

Abstract: This disclosure describes techniques and architectures for evaluating conversations. In some instances, conversations with users, virtual assistants, and others may be analyzed to identify potential risks within a language model that is employed by the virtual assistants and other entities. The potential risks may be evaluated by administrators, users, systems, and others to identify potential issues with the language model that need to be addressed. This may allow the language model to be improved and enhance user experience with the virtual assistants and others that employ the language model.

公开(公告)号：US11704477B2

公开(公告)日：2023-07-18

申请号：US17360718

申请日：2021-06-28

Applicant: Verint Americas Inc.

Inventor： Ian Roy Beaver ,  Cynthia Freeman ,  Jonathan Patrick Merriman ,  Abhinav Aggarwal

IPC: G06F17/00 ,  G06F40/117 ,  G06F40/35

CPC classification number: G06F40/117 ,  G06F40/35

Abstract: Attention weights in a hierarchical attention network indicate the relative importance of portions of a conversation between an individual at one terminal and a computer or a human agent at another terminal. Weighting the portions of the conversation after converting the conversation to a standard text format allows for a computer to graphically highlight, by color, font, or other indicator visible on a graphical user interface, which portions of a conversation led to an escalation of the interaction from an intelligent virtual assistant to a human customer service agent.

公开(公告)号：US11593713B2

公开(公告)日：2023-02-28

申请号：US16897400

申请日：2020-06-10

Applicant: Verint Americas Inc.

Inventor： Cynthia Freeman

IPC: G06N20/00 ,  G06F17/18

Abstract: Systems and methods are provided framework for automatically choosing the appropriate generalized linear model (GLM) given a time series of count data, and for anomaly detection on time series data. A dispersion parameter is determined and used to determine whether the count data is overdispersed data or underdispersed data. The overdispersed data or the underdispersed data is used to determine a GLM to apply on the dataset. Using the determined GLM on the data, anomalies can be determined.

公开(公告)号：US20220351099A1

公开(公告)日：2022-11-03

申请号：US17745422

申请日：2022-05-16

Applicant: Verint Americas Inc.

Inventor： Joseph Wayne Dumoulin ,  Cynthia Freeman ,  James DelloStritto

IPC: G06Q10/06 ,  G06F17/18 ,  G06Q30/00 ,  H04M15/00

Abstract: Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.

公开(公告)号：US12032543B2

公开(公告)日：2024-07-09

申请号：US18103023

申请日：2023-01-30

Applicant: Verint Americas Inc.

Inventor： Ian Roy Beaver ,  Cynthia Freeman ,  Jonathan Merriman

IPC: G06F16/215 ,  G06F16/2458

CPC classification number: G06F16/215 ,  G06F16/2474

Abstract: Disclosed are a framework and method for selecting an anomaly detection method for each of a plurality of class of time series based on characteristics a time series example that represents an expected form of data. The method provides classification of a given time series into one of known classes based on expected properties of the time series, filtering the set of possible detection methods based on the time series class, evaluating the remaining detection methods on the given time series using the specific evaluation metric and selecting and returning a recommended anomaly detection method based on the specific evaluation metric.

公开(公告)号：US20240020545A1

公开(公告)日：2024-01-18

申请号：US17812312

申请日：2022-07-13

Applicant: Verint Americas Inc.

Inventor： Jonathan Silverman ,  Nicholas Mortimer ,  Cynthia Freeman

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: The present disclosure describes methods and systems for selecting the forecasting algorithm to use for a prediction based on motifs. A motif is a pattern of interval values that is found to repeat in time series data. Time series data that includes historical demand data (e.g., average communication volume) for an entity at various time intervals in the past is received. The time series data is processed to identify motifs. For each identified motif, the forecasting algorithm that best predicts the historical demand data for time intervals associated with the motif is determined. Later, when the entity desires to receive a forecast for a future time interval, the motif associated with the future time interval is determined. The forecasting algorithm determined to best predict demand for the determined motif is then used to predict the demand for the future time interval.

公开(公告)号：US11822888B2

公开(公告)日：2023-11-21

申请号：US16546941

申请日：2019-08-21

Applicant: Verint Americas Inc.

Inventor： Ian Beaver ,  Cynthia Freeman ,  Andrew T. Pham

IPC: G06F40/30 ,  G06F40/274 ,  G06F3/04817 ,  G06F3/0482 ,  H04L67/131 ,  H04L67/1001 ,  G10L15/22 ,  G06F16/903 ,  G10L15/30 ,  G10L15/32

CPC classification number: G06F40/30 ,  G06F3/0482 ,  G06F3/04817 ,  G06F40/274 ,  G10L15/22 ,  H04L67/1001 ,  H04L67/131 ,  G06F16/90335 ,  G10L15/30 ,  G10L15/32 ,  G10L2015/226

Abstract: Features, libraries, and techniques are provided herein for determining the kinds of relational language that are present. Applying audio, emojis, and sentiment shifts as features may be used to determine whether the customer is providing backstory, whether there is ranting, etc. Textual features may be considered, as well as audio features may be considered.