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US Patent 11671444 Matching configuration items with machine learning

Patent 11671444 was granted and assigned to ServiceNow on June, 2023 by the United States Patent and Trademark Office.

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TimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
ServiceNow
ServiceNow
1
Current Assignee
ServiceNow
ServiceNow
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
116714441
Date of Patent
June 6, 2023
1
Patent Application Number
179313811
Date Filed
September 12, 2022
1
Patent Citations
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US Patent 7281170 Help desk systems and methods for use with communications networks
1
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US Patent 7505872 Methods and apparatus for impact analysis and problem determination
1
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US Patent 7596716 Method and system for managing networks
1
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US Patent 7617073 System and method for assessing and indicating the health of components
1
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US Patent 7676294 Visualization of workflow in an industrial automation environment
1
‌
US Patent 7840490 Comprehensive software licensing management system
1
‌
US Patent 7877783 System and method for secure communications with a remote software program
1
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US Patent 7890869 Network security visualization methods, apparatus and graphical user interfaces
1
...
Patent Citations Received
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US Patent 12095842 Server-side workflow improvement based on client-side data mining
2
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US Patent 12093517 Software architecture and user interface for process visualization
3
Patent Primary Examiner
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Harry H Kim
1
CPC Code
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G06N 3/08
1
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G06N 5/04
1
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G06N 20/00
1
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H04L 63/1433
1

A system includes persistent storage containing configuration items (CIs) representing discovered attribute values of computing resources associated with a managed network, and an application configured to perform operations, including obtaining test result data generated based on a third-party scanning system executing tests of a particular computing resource associated with the managed network. The test result data includes attribute values of the particular computing resource. The operations also include generating, by way of an embedding model and based on the attribute values, an embedding vector representing the attribute values, and comparing the embedding vector to a plurality of candidate embedding vectors, each representing the discovered attribute values of a corresponding CI of the CIs. The operations further include, based on comparing the embedding vector to the plurality of candidate embedding vectors, selecting a particular CI of the CIs, and updating the particular CI to additionally represent the test result data.

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