20240054058. ENSEMBLE MODELS FOR ANOMALY DETECTION simplified abstract (Zeta Global Corp.)

ENSEMBLE MODELS FOR ANOMALY DETECTION

Organization Name

Zeta Global Corp.

Inventor(s)

Danny Portman of Johns Creek GA (US)

Zachary Jones of Decatur GA (US)

ENSEMBLE MODELS FOR ANOMALY DETECTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240054058 titled 'ENSEMBLE MODELS FOR ANOMALY DETECTION

Simplified Explanation

The technology described in the patent application detects anomalies in media campaign configuration settings using deep learning models. The system identifies specific configuration settings that contribute to the detected anomalies, potentially preventing unsuccessful campaigns and minimizing wasted resources.

• Anomaly detection system uses deep learning models to detect anomalies in media campaign configuration settings.
• Multiple deep learning models can be combined into an ensemble model to improve anomaly predictions.
• System reviews configuration settings before campaigns run to reduce unsuccessful campaigns and wasted resources.

Potential Applications

• Marketing and advertising industries
• Digital media agencies
• E-commerce platforms

Problems Solved

• Identifying anomalies in media campaign configuration settings
• Preventing unsuccessful campaigns
• Minimizing wasted resources

Benefits

• Improved accuracy in anomaly detection
• Cost savings from avoiding unsuccessful campaigns
• Efficient allocation of resources

Original Abstract Submitted

the subject technology detects anomalies in media campaign configuration settings. the anomaly detection system may leverage one or more deep learning models to detect anomalies and identify particular configuration settings that contribute to the detected anomalies. in various embodiments, two or more of the deep learning models may be combined into an ensemble model that boosts the accuracy of anomaly predictions made by the anomaly detection system. the anomaly detection system may review the configuration settings of media campaigns during the configuration process and before the media campaigns run on a publication system in order to reduce the amount of unsuccessful campaigns and minimize the amount of wasted resources spent on running campaigns that have a low likelihood of achieving user defined goals.