18416869. DATA ANOMALY DETECTION simplified abstract (Microsoft Technology Licensing, LLC)

DATA ANOMALY DETECTION

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Roman Batoukov of Redmond WA (US)

Richard Wydrowski of Bellevue WA (US)

Sai Sankalp Arrabolu of Kirkland WA (US)

Zeqiang Wang of Redmond WA (US)

Lech Gudalewicz of Sammamish WA (US)

Keiji Kanazawa of Seattle WA (US)

Benjamin J. Lofton of Seattle WA (US)

Thomas W. Potthast of Sammamish WA (US)

Suren Aghajanyan of Bellevue WA (US)

Khoa Tran of Redmond WA (US)

Jian Zhang of Bellevue WA (US)

DATA ANOMALY DETECTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18416869 titled 'DATA ANOMALY DETECTION

Simplified Explanation

The abstract describes a patent application for systems and methods for data anomaly detection, which includes recommending algorithms based on the type of workload, providing assisted parameter tuning for detected anomalies, and processing data using a user-selected algorithm that is parameter tuned.

• Recommending algorithms based on workload:

   - Algorithms are recommended from a set based on the type of workload for processing time series data.
   - This helps in selecting the most suitable algorithm for efficient anomaly detection.

• Assisted parameter tuning for detected anomalies:

   - Calibration and tuning of parameters are provided for detected anomaly alerts.
   - This ensures that the system is optimized for accurate anomaly detection.

• Processing data using user-selected algorithm:

   - The received time series data is processed using a user-selected algorithm that is parameter tuned.
   - This results in more efficient and reliable anomaly detection.

Potential Applications

The technology can be applied in various industries such as finance, healthcare, cybersecurity, and manufacturing for detecting anomalies in data streams.

Problems Solved

1. Efficient anomaly detection in time series data. 2. Recommendation of suitable algorithms based on workload for processing data.

Benefits

1. Improved accuracy in anomaly detection. 2. Enhanced efficiency in processing time series data. 3. Customized parameter tuning for optimized performance.

Potential Commercial Applications

Optimized anomaly detection systems for finance companies. SEO optimized title: "Commercial Applications of Optimized Anomaly Detection Systems"

Possible Prior Art

One possible prior art could be anomaly detection systems that recommend algorithms based on data patterns and trends.

What are the limitations of the recommended algorithms in anomaly detection?

The article does not address the potential limitations of the recommended algorithms in anomaly detection.

How does the assisted parameter tuning process work in detail?

The article does not provide a detailed explanation of how the assisted parameter tuning process works for detected anomalies.

Original Abstract Submitted

Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.