View source for 17938806. SYSTEM AND METHOD FOR DATA DRIFT DETECTION WHILE PERFORMING ANOMALY DETECTION simplified abstract (Dell Products L.P.)

=SYSTEM AND METHOD FOR DATA DRIFT DETECTION WHILE PERFORMING ANOMALY DETECTION=


==Organization Name==

[[:Category:Dell Products L.P.|Dell Products L.P.]]


[[Category:Dell Products L.P.]]

==Inventor(s)==

[[:Category:OFIR Ezrielev of Beer Sheva (IL)|OFIR Ezrielev of Beer Sheva (IL)]][[Category:OFIR Ezrielev of Beer Sheva (IL)]]

[[:Category:NADAV Azaria of Beer Sheva (IL)|NADAV Azaria of Beer Sheva (IL)]][[Category:NADAV Azaria of Beer Sheva (IL)]]

[[:Category:AVITAN Gefen of Tel Aviv (IL)|AVITAN Gefen of Tel Aviv (IL)]][[Category:AVITAN Gefen of Tel Aviv (IL)]]

==SYSTEM AND METHOD FOR DATA DRIFT DETECTION WHILE PERFORMING ANOMALY DETECTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17938806 titled 'SYSTEM AND METHOD FOR DATA DRIFT DETECTION WHILE PERFORMING ANOMALY DETECTION

==Simplified Explanation==

Methods and systems for detecting data drift while performing anomaly detection in a distributed environment are disclosed. An anomaly detector and one or more data collectors work together to detect anomalies in data using continuous and quantized inference models. The system compares the outputs of these models to determine if the continuous model has adapted to data drift over time through re-training.

* Anomaly detection system in a distributed environment
* Utilizes continuous and quantized inference models
* Detects anomalies in data and data drift
* Compares outputs of inference models to assess adaptation to data drift
* Discards data after anomaly detection to remove it from the system

== Potential Applications ==
This technology can be applied in various fields such as:
* Cybersecurity
* Fraud detection
* Predictive maintenance
* Quality control in manufacturing

== Problems Solved ==
The technology addresses the following issues:
* Detecting anomalies in distributed data
* Monitoring and adapting to data drift over time
* Improving the accuracy of anomaly detection systems

== Benefits ==
The benefits of this technology include:
* Enhanced anomaly detection capabilities
* Improved adaptability to changing data patterns
* Increased accuracy in identifying anomalies and data drift

== Potential Commercial Applications ==
This technology has potential commercial applications in:
* Financial institutions
* Healthcare organizations
* E-commerce platforms
* Manufacturing companies

== Possible Prior Art ==
Prior art in anomaly detection and data drift detection includes:
* Research papers on continuous and quantized inference models
* Patents related to anomaly detection in distributed systems

=== What are the limitations of the continuous and quantized inference models in detecting data drift?
The abstract does not provide information on the limitations of the continuous and quantized inference models in detecting data drift.

=== How does the system handle false positives in anomaly detection?
The abstract does not mention how the system handles false positives in anomaly detection.


==Original Abstract Submitted==

Methods and systems for detecting data drift while performing anomaly detection in a distributed environment are disclosed. To perform anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using a continuous inference model. To detect data drifts in data from the one or more data collectors, the anomaly detector may also detect anomalies in data obtained from one or more data detectors using a quantized inference model. The output of the continuous inference model may be compared to the output of the quantized inference model to determine whether the continuous inference model has adapted to data drift over time through re-training. Following anomaly detection and/or data drift detection, the data may be discarded to remove the data from the anomaly detector.



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[[Category:H04L41/16]]