Google llc (20240193035). Point Anomaly Detection simplified abstract

Point Anomaly Detection

Organization Name

google llc

Inventor(s)

Zichuan Ye of Mountain View CA (US)

Jiashang Liu of Kirkland WA (US)

Forest Elliott of Mountain View CA (US)

Amir Hormati of Mountain View CA (US)

Xi Cheng of Kirkland WA (US)

Mingge Deng of Kirkland WA (US)

Point Anomaly Detection - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240193035 titled 'Point Anomaly Detection

Simplified Explanation: The patent application describes a method for detecting anomalies in point data values by training a model and determining anomalous values based on their variance.

• Key Features and Innovation:

   * Receiving a query for anomaly detection in point data values.
   * Training a model using the set of point data values.
   * Determining anomalous values based on their variance exceeding a threshold.
   * Reporting the anomalous values to the user.

• Potential Applications:

   * Data quality assurance in various industries.
   * Fraud detection in financial transactions.
   * Monitoring sensor data for anomalies in IoT devices.

• Problems Solved:

   * Efficient detection of anomalous data points.
   * Improving data accuracy and reliability.
   * Enhancing decision-making based on reliable data.

• Benefits:

   * Early detection of anomalies for proactive action.
   * Improved data integrity and trustworthiness.
   * Enhanced overall system performance.

• Commercial Applications:

   * "Anomaly Detection in Point Data Values: Enhancing Data Quality and Reliability"
   * Potential use in industries such as finance, healthcare, and manufacturing.
   * Market implications include improved operational efficiency and risk mitigation.

• Prior Art:

   Research on anomaly detection algorithms in data analytics and machine learning.

• Frequently Updated Research:

   Ongoing advancements in anomaly detection techniques and models for point data values.

Questions about Anomaly Detection in Point Data Values: 1. What are the key challenges in anomaly detection for point data values, and how does this method address them? 2. How does the threshold for variance values impact the accuracy of anomaly detection in this method?

Original Abstract Submitted

a method includes receiving a point data anomaly detection query from a user. the query requests the data processing hardware to determine a quantity of anomalous point data values in a set of point data values. the method includes training a model using the set of point data values. for at least one respective point data value in the set of point data values, the method includes determining, using the trained model, a variance value for the respective point data value and determining that the variance value satisfies a threshold value. based on the variance value satisfying the threshold value, the method includes determining that the respective point data value includes an anomalous point data value. the method includes reporting the determined anomalous point data value to the user.