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US Patent 10045218 Anomaly detection in streaming telephone network data

Patent 10045218 was granted and assigned to Argyle Data on August, 2018 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Argyle Data
Argyle Data
Current Assignee
Argyle Data
Argyle Data
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10045218
Date of Patent
August 7, 2018
Patent Application Number
15661531
Date Filed
July 27, 2017
Patent Citations Received
‌
US Patent 12088473 Method, device and system for enhancing predictive classification of anomalous events in a cloud-based application acceleration as a service environment
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US Patent 12026076 Method and system for proactive client relationship analysis
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US Patent 12079337 Systems and methods for identifying malware injected into a memory of a computing device
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US Patent 11502900 Systems and methods for modifying device operation based on datasets generated using machine learning techniques
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US Patent 10484532 System and method detecting fraud using machine-learning and recorded voice clips
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US Patent 11553080 Detecting fraud using machine-learning and recorded voice clips
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US Patent 10771313 Using random forests to generate rules for causation analysis of network anomalies
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US Patent 10826927 Systems and methods for data exfiltration detection
...
Patent Primary Examiner
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Brandon J Miller
Patent abstract

In one example, a method includes receiving a feature vector that characterizes a call history for a telephone network subscriber, wherein the feature vector comprises respective categorical values for one or more categorical features and respective continuous values for one or more continuous features, and applying, to the categorical values, a first algorithm to determine a categorical score for the feature vector. The example method further includes applying, to the continuous values, an isolation forest algorithm to determine a continuous score for the feature vector, and outputting, in response to determining at least one of the categorical score for the feature vector and the continuous score for the feature vector indicate the feature vector is anomalous, an indication that the feature vector is anomalous.

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