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US Patent 11841786 Predictive anomaly detection framework

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
0
Patent Number
118417860
Patent Inventor Names
Abdul Hadi Shakir0
Subash Prabanantham0
Raghuveer Chanda0
Vipul Valamjee0
Himanshu Ojha0
Date of Patent
December 12, 2023
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Patent Application Number
175567920
Date Filed
December 20, 2021
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Patent Citations
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US Patent 7792770 Method to indentify anomalous data using cascaded K-Means clustering and an ID3 decision tree
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US Patent 9300684 Methods and systems for statistical aberrant behavior detection of time-series data
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US Patent 9727533 Detecting anomalies in a time series
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US Patent 9772896 Identifying intervals of unusual activity in information technology systems
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US Patent 10061677 Fast automated detection of seasonal patterns in time series data without prior knowledge of seasonal periodicity
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US Patent 10102056 Anomaly detection using machine learning
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US Patent 10263833 Root cause investigation of site speed performance anomalies
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US Patent 10402244 Detection of abnormal resource usage in a data center
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Patent Primary Examiner
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Elmira Mehrmanesh
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CPC Code
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G06F 11/3447
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G06N 20/20
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G06F 11/0751
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G06F 11/3075
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G06F 11/3452
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Patent abstract

Embodiments of the invention are directed to techniques for detecting anomalous values in data streams using forecasting models. In some embodiments, a computer can receive a value of a data stream comprising a plurality of data values, where the received value corresponds to a time interval and previously received values each correspond to a previous time interval. Models can be selected based on the time interval, where each of the models has a different periodicity. For each of the selected models, the computer may generate a score by generating a prediction value based on the model and generating the score based on the prediction value and the received value. A final score can then be generated based on the scores. Next, a score threshold can be generated. If the final score exceeds the score threshold, the computer may generate a notification that indicates that the data value is an anomaly.

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