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公开(公告)号：US10521210B1
公开(公告)日：2019-12-31
申请号：US15968193
申请日：2018-05-01
发明人： Prasanth Nandanuru , Andrew J. Garner, IV , Kory Bunya , Eduardo Dela Torre , Dennis Montenegro , Yevanna M. Yejjala , Dinakar Channakal Krishnappa , Chinababu Kona , Sai Krishna Verma Sundaragiri , Priyanka Amara , Shiju Thukalankuzhy John
摘要： Systems and methods for converting an application to new programming language or framework is provided. A source application written in a legacy framework is analyzed and divided into smaller segments of code. The smaller segments are evaluated for quality in view of best practices design for writing applications. A rubric is determined for each segment and compared to a best practice threshold. Segments can be evaluated for features that can be passed through a model. The model converts the features into code in the new programming language. Machine learning and rules databases are updated with details of the conversion.
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公开(公告)号：US11269605B1
公开(公告)日：2022-03-08
申请号：US16682706
申请日：2019-11-13
发明人： Prasanth Nandanuru , Andrew J. Garner, IV , Kory Bunya , Eduardo Dela Torre , Dennis Montenegro , Yevanna M. Yejjala , Dinakar Channakal Krishnappa , Chinababu Kona , Sai Krishna Verma Sundaragiri , Priyanka Amara , Shiju Thukalankuzhy John
摘要： An application can be converted to new programming language or framework. A source application written for a legacy framework is analyzed and divided into smaller segments of code. The smaller segments are evaluated for quality in view of best practices design for writing applications. A rubric is determined for each segment and compared to a best practice threshold. Segments can be evaluated for features that can be passed through a model. The model converts the features into new code adapted for target framework. Machine learning and rules databases are updated with details of the conversion.
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公开(公告)号：US10659909B1
公开(公告)日：2020-05-19
申请号：US16298351
申请日：2019-03-11
发明人： Gail Elizabeth Stuart , Andrew J. Garner, IV , Daniel S. Sumner , Prasanna Kumar Ammiraju , Dibyendu Chatterjee , Kshitindra Kumar Jain , Rameshchandra Bhaskar Ketharaju , Dinakar Channakal Krishnappa , Ravindar Rao Perala , Dinesh Sangtani , Mohan Gopal Kanala , Prashanth Kumar Merugu
摘要： A method may include receiving, from a customer device, contextual location information about a customer and an interaction type, determining a location-based recommendation and an urgency level for the customer using the contextual location information, generating a routing protocol for communication with the customer based on the interaction type and the urgency level, and providing a communication to the customer according to the routing protocol, the communication including the location-based recommendation.
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公开(公告)号：US10231076B1
公开(公告)日：2019-03-12
申请号：US15268223
申请日：2016-09-16
发明人： Gail Elizabeth Stuart , Andrew J. Garner, IV , Daniel S. Sumner , Prasanna Kumar Ammiraju , Dibyendu Chatterjee , Kshitindra Kumar Jain , Rameshchandra Bhaskar Ketharaju , Dinakar Channakal Krishnappa , Ravindar Rao Perala , Dinesh Sangtani , Mohan Gopal Kanala , Prashanth Kumar Merugu
摘要： A method may include receiving, from a customer device, contextual location information about a customer and an interaction type, determining a location-based recommendation and an urgency level for the customer using the contextual location information, generating a routing protocol for communication with the customer based on the interaction type and the urgency level, and providing a communication to the customer according to the routing protocol, the communication including the location-based recommendation.
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公开(公告)号：US09971581B1
公开(公告)日：2018-05-15
申请号：US15015797
申请日：2016-02-04
发明人： Prasanth Nandanuru , Priyanka Amara , Shiju Thukalankuzhy John , Andrew J. Garner, IV , Kory Bunya , Eduardo Dela Torre , Dennis Montenegro , Yevanna M. Yejjala , Dinakar Channakal Krishnappa , Chinababu Kona , Sai Krishna Verma Sundaragiri
IPC分类号： G06F9/45
摘要： Systems and methods for converting an application to new programming language or framework is provided. A source application written in a legacy framework is analyzed and divided into smaller segments of code. The smaller segments are evaluated for quality in view of best practices design for writing applications. A rubric is determined for each segment and compared to a best practice threshold. Segments can be evaluated for features that can be passed through a model. The model converts the features into code in the new programming language. Machine learning and rules databases are updated with details of the conversion.
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