公开(公告)号：US20230161660A1

公开(公告)日：2023-05-25

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 ,  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：US20240004847A1

公开(公告)日：2024-01-04

申请号：US18466796

申请日：2023-09-13

Applicant: Capital One Services, LLC

Inventor： Vannia GONZALEZ MACIAS ,  Scott GARCIA ,  Peter TERRANA

IPC: G06F16/215 ,  G06F16/2458

CPC classification number: G06F16/215 ,  G06F16/2474

Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

公开(公告)号：US20230259504A1

公开(公告)日：2023-08-17

申请号：US18189174

申请日：2023-03-23

Applicant: Capital One Services, LLC

Inventor： Vannia GONZALEZ MACIAS ,  Scott GARCIA ,  Peter TERRANA

IPC: G06F16/23 ,  G06F7/08

CPC classification number: G06F16/2365 ,  G06F7/08

Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

公开(公告)号：US20240220480A1

公开(公告)日：2024-07-04

申请号：US18608327

申请日：2024-03-18

Applicant: Capital One Services, LLC

Inventor： Vannia GONZALEZ MACIAS ,  Scott GARCIA ,  Peter TERRANA

IPC: G06F16/23 ,  G06F7/08

CPC classification number: G06F16/2365 ,  G06F7/08

Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

公开(公告)号：US20240152419A1

公开(公告)日：2024-05-09

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

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.