公开(公告)号：US11579958B2

公开(公告)日：2023-02-14

申请号：US17239342

申请日：2021-04-23

Applicant: Capital One Services, LLC

Inventor： Vannia Gonzalez Macias ,  Paul Cho ,  Rahul Gupta ,  Scott Garcia ,  Adithya Ramanathan

IPC: G06F11/07 ,  G06N20/00 ,  G06F40/30 ,  G06F40/289

Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

公开(公告)号：US20220342745A1

公开(公告)日：2022-10-27

申请号：US17239342

申请日：2021-04-23

Applicant: Capital One Services, LLC

Inventor： Vannia Gonzalez Macias ,  Paul Cho ,  Rahul Gupta ,  Scott Garcia ,  Adithya Ramanathan

IPC: G06F11/07 ,  G06F40/289 ,  G06F40/30 ,  G06N20/00

Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

公开(公告)号：US12174694B2

公开(公告)日：2024-12-24

申请号：US18415310

申请日：2024-01-17

Applicant: Capital One Services, LLC

Inventor： Vannia Gonzalez Macias ,  Paul Cho ,  Rahul Gupta ,  Scott Garcia ,  Adithya Ramanathan

IPC: G06F11/07 ,  G06F40/289 ,  G06F40/30 ,  G06N20/00

Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

公开(公告)号：US11914462B2

公开(公告)日：2024-02-27

申请号：US18152590

申请日：2023-01-10

Applicant: Capital One Services, LLC

Inventor： Vannia Gonzalez Macias ,  Paul Cho ,  Rahul Gupta ,  Scott Garcia ,  Adithya Ramanathan

IPC: G06F11/07 ,  G06N20/00 ,  G06F40/30 ,  G06F40/289

CPC classification number: G06F11/0781 ,  G06F40/289 ,  G06F40/30 ,  G06N20/00

Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.