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US Patent 11907658 User-agent anomaly detection using sentence embedding

Patent 11907658 was granted and assigned to PayPal on February, 2024 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
PayPal
PayPal
0
Current Assignee
PayPal
PayPal
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119076580
Patent Inventor Names
Zhe Chen0
Quan Jin Ferdinand Tang0
Shanshan Peng0
Serafin Trujillo0
Solomon kok how Teo0
Kenneth Bradley Snyder0
Omkumar Mahalingam0
Hewen Wang0
...
Date of Patent
February 20, 2024
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Patent Application Number
173089310
Date Filed
May 5, 2021
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Patent Citations
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US Patent 11356472 Systems and methods for using machine learning for geographic analysis of access attempts
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US Patent 11418461 Architecture for dynamic management of dialog message templates
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US Patent 11463475 Click-to-call fraud detection
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US Patent 9690936 Multistage system and method for analyzing obfuscated content for malware
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US Patent 9876825 Monitoring user authenticity
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US Patent 10007786 Systems and methods for detecting malware
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US Patent 10958779 Machine learning dataset generation using a natural language processing technique
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US Patent 11003773 System and method for automatically generating malware detection rule recommendations
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Patent Citations Received
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US Patent 12131819 Utilizing predictive modeling to identify anomaly events
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Patent Primary Examiner
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Martin Lerner
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CPC Code
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G06F 21/56
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G06F 40/284
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G06F 21/566
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G06F 21/55
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G06F 21/30
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G06F 40/279
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G06F 21/564
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Patent abstract

Systems and methods for user-agent anomaly detection are disclosed. In one embodiment, a user-agent string may be embedded into a numerical data vector representation using a sentence embedding algorithm (e.g., FastText). A predictive score may be calculated based on the numerical data vector representation and using a probability distribution function model that models a likelihood of occurrence of the observed user-agent based on patterns learned from historic payload data (e.g., a Gaussian Mixture Model). The predictive score may be compared to a threshold and, based on the comparison, it may be determined whether the user-agent is fraudulent.

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