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US Patent 11609838 System to track and measure machine learning model efficacy

Patent 11609838 was granted and assigned to PayPal on March, 2023 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
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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
116098380
Patent Inventor Names
Jonathan Doering0
Sruthi Yapalapalli0
Sriharsha Vogeti0
Lokesh Nyati0
Date of Patent
March 21, 2023
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Patent Application Number
170241330
Date Filed
September 17, 2020
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Patent Citations
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US Patent 10505825 Automatic creation of related event groups for IT service monitoring
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US Patent 10740692 Machine-learning and deep-learning techniques for predictive ticketing in information technology systems
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Patent Citations Received
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US Patent 12066918 System to track and measure machine learning model efficacy
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Patent Primary Examiner
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John H Le
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CPC Code
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G06F 2201/865
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G06N 20/00
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G06F 11/3612
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G06F 11/3616
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G06F 11/3452
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G06F 11/323
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G06F 11/302
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G06F 11/0754
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Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features. In various cases, the second set of statistical metrics can characterize trends or distributions of input data associated with the machine learning model. In various aspects, the system can store the first set of statistical metrics and the second set of statistical metrics in a data warehouse that is accessible to an operator. In various embodiments, the system can render the first set of statistical metrics and the second set of statistical metrics on an electronic interface, such that the first set of statistical metrics and the second set of statistical metrics are viewable to the operator.

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