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US Patent 11966326 Development test automation framework

Patent 11966326 was granted and assigned to Bank of America on April, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
Bank of America
Bank of America
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Current Assignee
Bank of America
Bank of America
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119663260
Patent Inventor Names
Kurt R. Schultz0
Nadeem Panjwani0
Charanjit Gurnasinghani0
Date of Patent
April 23, 2024
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Patent Application Number
178689120
Date Filed
July 20, 2022
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Patent Citations
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US Patent 10540272 Software test automation system and method
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US Patent 10007595 Intelligent software test augmenting
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US Patent 10318412 Systems, methods, and apparatus for dynamic software generation and testing
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US Patent 10423522 System and method for detecting an error in software
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US Patent 11263117 System for intelligent code update for a test automation engine
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US Patent 11263119 Just in time scriptless test automation selectors and interactions
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US Patent 11301366 Test automation systems and methods using logical identifiers
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US Patent 11314627 Test automation system for distributed heterogenous environments
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...
Patent Primary Examiner
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Douglas M Slachta
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CPC Code
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G06F 11/3692
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G06F 11/36
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G06F 11/3688
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G06N 5/022
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Patent abstract

Aspects of the disclosure relate to automatically detecting, analyzing, and correcting errors in an application. The computing platform may identify errors in an application, and subsequently correct the errors based on corresponding actions that have been determined to correct the errors. The computing platform may determine a confidence score that corresponds to a particular action, representing the level of confidence that the particular action will successfully correct the corresponding error. The computing platform may set a predetermined threshold range that is compared to the confidence score, in which a confidence score greater than the threshold range will cause the computing platform to automatically correct the error. The computing platform may input results and feedback into a machine learning model to further refine the accuracy and reliability of the computing platform over time.

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