Stripe, inc. (20240281816). SYSTEMS AND METHODS FOR ANOMALY PREDICTION simplified abstract

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SYSTEMS AND METHODS FOR ANOMALY PREDICTION

Organization Name

stripe, inc.

Inventor(s)

Meidan Bu of Bellevue WA (US)

Ramy Shoker of Seattle WA (US)

Adam Behrens of Medron MA (US)

SYSTEMS AND METHODS FOR ANOMALY PREDICTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240281816 titled 'SYSTEMS AND METHODS FOR ANOMALY PREDICTION

Simplified Explanation: The patent application describes systems and methods for predicting anomalies in customer data using machine learning models.

  • An anomaly detection system identifies data generated for a customer.
  • A first set of features for the customer is identified based on the data.
  • An anomaly evaluation is performed based on detecting a criterion.
  • The anomaly evaluation includes identifying a customer segment, a distribution of values for the segment, and determining if a value satisfies a threshold.
  • If the value satisfies the threshold, a machine learning model is invoked to predict an anomaly for the customer.
  • A notification may be transmitted to trigger an action for addressing the anomaly.

Key Features and Innovation:

  • Identification of customer data features.
  • Anomaly evaluation based on customer segment and value distribution.
  • Machine learning model prediction for anomalies.
  • Notification system for addressing anomalies.

Potential Applications: The technology can be applied in various industries such as finance, healthcare, cybersecurity, and retail for predicting anomalies in customer data.

Problems Solved: The technology helps in early detection of anomalies in customer data, allowing for proactive measures to address potential issues before they escalate.

Benefits:

  • Improved data security and fraud detection.
  • Enhanced customer experience through proactive issue resolution.
  • Cost savings by preventing potential losses due to anomalies.

Commercial Applications: Predictive anomaly detection technology can be utilized by banks, insurance companies, e-commerce platforms, and other businesses to safeguard customer data and enhance operational efficiency.

Questions about Anomaly Prediction Technology: 1. How does the anomaly detection system identify customer segments? 2. What types of actions can be triggered in response to an identified anomaly?


Original Abstract Submitted

systems and methods for anomaly prediction are disclosed. an anomaly detection system identifies data generated for a customer. a first set of features for the customer are identified based on the data. the system performs an anomaly evaluation based on detecting a criterion. the anomaly evaluation may include identifying a customer segment based on the first set of features; identifying a distribution of values for the customer segment; determining, based on the distribution of values, whether a value associated with the first set of features satisfies a threshold; and in response to the determining that the value satisfies the threshold, invoking a machine learning model for predicting an anomaly for the customer based on at least a portion of the data. a notification may be transmitted about the anomaly to trigger an action for addressing the anomaly.

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