Patents by Inventor Michael Elton Nidd
Michael Elton Nidd has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20230153225Abstract: In an approach to risk prediction for bug-introducing changes, a computer retrieves one or more historic pull requests. A computer determines a unique file linking for each file included in the historic pull requests. A computer generates a file risk dataset. A computer performs chronological partitioning on the file risk dataset. A computer determines bug-introducing changes in the file risk dataset. A computer computes a collaborative file association between two or more of the files in the file risk dataset. A computer labels each of the files in the file risk dataset with an associated risk of introducing a bug. A computer generates a labelled file risk inducing ground truth dataset. A computer inputs the labelled file risk inducing ground truth dataset to a file risk prediction model. A computer extracts pull request features from the historic pull requests. A computer generates a pull request risk prediction model.Type: ApplicationFiled: November 16, 2021Publication date: May 18, 2023Inventors: Amar Prakash Azad, Harshit Kumar, Raghav Batta, Michael Elton Nidd, Larisa Shwartz, PRITAM GUNDECHA, Alberto Giammaria
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Patent number: 11645558Abstract: A method, a computer system, and a computer program product for mapping operational records to a topology graph. Embodiments of the present invention may include generating an event frequent pattern using operational records. Embodiments of the present invention may include integrating topology-based event frequent patterns. Embodiments of the present invention may include mapping the operational records with an embedding engine. Embodiments of the present invention may include predicting incident events. Embodiments of the present invention may include receiving labeled patterns to the embedding engine for an active learning cycle.Type: GrantFiled: May 8, 2020Date of Patent: May 9, 2023Assignee: International Business Machines CorporationInventors: Qing Wang, Larisa Shwartz, Srinivasan Parthasarathy, Jinho Hwang, Tengfei Ma, Michael Elton Nidd, Frank Bagehorn, Jakub Krchák, Altynbek Orumbayev, Michal Mýlek, Ota Sandr, Tomá{hacek over (s)} Ondrej
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Patent number: 11645188Abstract: In an approach to risk prediction for bug-introducing changes, a computer retrieves one or more historic pull requests. A computer determines a unique file linking for each file included in the historic pull requests. A computer generates a file risk dataset. A computer performs chronological partitioning on the file risk dataset. A computer determines bug-introducing changes in the file risk dataset. A computer computes a collaborative file association between two or more of the files in the file risk dataset. A computer labels each of the files in the file risk dataset with an associated risk of introducing a bug. A computer generates a labelled file risk inducing ground truth dataset. A computer inputs the labelled file risk inducing ground truth dataset to a file risk prediction model. A computer extracts pull request features from the historic pull requests. A computer generates a pull request risk prediction model.Type: GrantFiled: November 16, 2021Date of Patent: May 9, 2023Assignee: International Business Machines CorporationInventors: Amar Prakash Azad, Harshit Kumar, Raghav Batta, Michael Elton Nidd, Larisa Shwartz, Pritam Gundecha, Alberto Giammaria
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Publication number: 20230004761Abstract: An approach for generating actionable explanations of change request classifications may be presented. A model may generate features associated with a change request may be disclosed. The model may be trained with historical change requests that have been labeled risky or not risky. The change request may be classified as risky or not risky. Candidate historical change requests with the same classification as the change request and occupying similar feature space as the change request may be identified from a historical change request repository. One or more features which had the most significant impact on the classification may be identified. A candidate historical change request with at least one significant feature impacting classification may be identified.Type: ApplicationFiled: June 30, 2021Publication date: January 5, 2023Inventors: Raghav Batta, Michael Elton Nidd, Larisa Shwartz, PRITAM GUNDECHA, Rama Kalyani T. Akkiraju, Amar Prakash Azad, Harshit Kumar
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Patent number: 11487537Abstract: In an approach to linking operational data with issues, a new event is received. The new event is associated to a story, where the story is related to an identified problem within the system, and further where the new event is associated with the story using machine learning techniques. The story is associated to related change requests based on a similarity between the story and related change requests, where the similarity between the story and the related change requests is associated using the machine learning techniques. A cost is calculated for the story. Responsive to associating the new event with a specific change request, the priority of the specific change request is updated based on the cost for the story.Type: GrantFiled: November 18, 2020Date of Patent: November 1, 2022Assignee: International Business Machines CorporationInventors: Michael Elton Nidd, Altynbek Orumbayev, Jinho Hwang, Larisa Shwartz, Jakub Krchak, Qing Wang, Frank Bagehorn, Ota Sandr, Tomas Ondrej, Srinivasan Parthasarathy, Michal Mylek
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Publication number: 20220303302Abstract: A method, a computer system, and a computer program product for security risk analysis is provided. Embodiments of the present invention may include collecting operational data. Embodiments of the present invention may include building pipelines. Embodiments of the present invention may include localizing security issues using the operational data on an unsupervised model. Embodiments of the present invention may include constructing a semantic graph using shift-left data. Embodiments of the present invention may include constructing a mapping between the operational data and the shift-left data. Embodiments of the present invention may include clustering collected datasets. Embodiments of the present invention may include creating an active learning cycle using ground truth.Type: ApplicationFiled: March 22, 2021Publication date: September 22, 2022Inventors: Jinho HWANG, Larisa Shwartz, Raghav Batta, Michael Elton Nidd, Jakub Krchak
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Publication number: 20220230090Abstract: Systems, computer-implemented methods, and computer program products to facilitate proactive operational risk assessment of a proposed change in a computing environment are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component that identifies change events in historic operational data that induced one or more incidents in a computing environment. The computer executable components further comprise an assessment component that employs a model to assign a change risk assessment score to a defined change in the computing environment based on the change events.Type: ApplicationFiled: January 15, 2021Publication date: July 21, 2022Inventors: Raghav Batta, Michael Elton Nidd, Larisa Shwartz, Jinho Hwang, Harshit Kumar
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Publication number: 20220156065Abstract: In an approach to linking operational data with issues, a new event is received. The new event is associated to a story, where the story is related to an identified problem within the system, and further where the new event is associated with the story using machine learning techniques. The story is associated to related change requests based on a similarity between the story and related change requests, where the similarity between the story and the related change requests is associated using the machine learning techniques. A cost is calculated for the story. Responsive to associating the new event with a specific change request, the priority of the specific change request is updated based on the cost for the story.Type: ApplicationFiled: November 18, 2020Publication date: May 19, 2022Inventors: Michael Elton Nidd, Altynbek Orumbayev, Jinho HWANG, Larisa Shwartz, Jakub Krchak, Qing Wang, Frank Bagehorn, Ota Sandr, Tomas Ondrej, Srinivasan Parthasarathy, Michal Mylek
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Patent number: 11314575Abstract: An approach to recommending corrective action to computing system event errors. The approach may include generating a textual description of an event error. The approach may include transforming the textual description into feature vectors with a domain-specific word embedding module. The approach may also include generating a recommendation to correct the event error based on an analysis of the feature vectors. Additionally, the recommendation may be presented for verification.Type: GrantFiled: August 3, 2020Date of Patent: April 26, 2022Assignee: International Business Machines CorporationInventors: Qing Wang, Larisa Shwartz, Jinho Hwang, Srinivasan Parthasarathy, Michael Elton Nidd, Frank Bagehorn, Jakub Krchák, Tomás Ondrej, Altynbek Orumbayev, Michal Mýlek, Ota Sandr
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Publication number: 20220091912Abstract: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can dynamically determine one or more endpoints to fulfill a user request. Embodiments of the present invention can select the dynamically determined one or more endpoints as the one or more endpoints that fulfill the user request. Embodiments of the present invention can execute the selected one or more endpoints to fulfill the user request.Type: ApplicationFiled: September 21, 2020Publication date: March 24, 2022Inventors: Larisa Shwartz, Qing Wang, Jinho Hwang, Srinivasan Parthasarathy, Michael Elton Nidd, Frank Bagehorn, Ota Sandr, Tomas Ondrej, Altynbek Orumbayev, Jakub Krchak, Michal Mylek
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Publication number: 20220083876Abstract: A method, a computer system, and a computer program product for a shiftleft topology construction is provided. Embodiments of the present invention may include collecting datasets. Embodiments of the present invention may include extracting topological entities from the datasets. Embodiments of the present invention may include correlating a plurality of data from the topological entities. Embodiments of the present invention may include mapping the topological entities. Embodiments of the present invention may include marking entry points for a plurality of subgraphs of the topological entities. Embodiments of the present invention may include constructing a topology graph.Type: ApplicationFiled: September 17, 2020Publication date: March 17, 2022Inventors: Jinho HWANG, Larisa Shwartz, Srinivasan Parthasarathy, Qing Wang, Michael Elton Nidd, Frank Bagehorn, Jakub Krchák, Ota Sandr, Tomás Ondrej, Michal Mýlek, Altynbek Orumbayev, Randall M George
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Patent number: 11262990Abstract: A computer implemented method for identifying an application topology includes identifying a sandbox environment corresponding to an application of interest, analyzing the sandbox environment to identify a set of communication links between services within the sandbox environment indicating a first topology, identifying a production system corresponding to the application of interest, querying the production system to identify a set of structural dependencies indicating a second topology, and creating a complete topology of the cloud application by combining the first topology and the second topology. A computer program product and computer system for identifying an application topology are additionally disclosed herein.Type: GrantFiled: May 26, 2020Date of Patent: March 1, 2022Assignee: International Business Machines CorporationInventors: Srinivasan Parthasarathy, Jinho Hwang, Qing Wang, Larisa Shwartz, Michael Elton Nidd, Frank Bagehorn, Jakub Krchák, Altynbek Orumbayev, Michal Mýlek, Ota Sandr, Tomá{hacek over (s)} Ondrej
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Patent number: 11265288Abstract: Various embodiments manage the migration of servers. In one embodiment, a set of server-level dependency information is obtained for servers to be migrated from a source computing environment to a target computing environment. A set of network configuration data is obtained for a plurality of network devices associated with the servers. The set of server-level dependency information is updated to include one or more additional dependencies of at least one of the servers based on the set of network configuration data. Updating the set of server-level dependency information generates an updated set of dependency information. The servers are assigned to multiple migration groups based on the updated set of dependency information. The migration groups optimize cross-group dependencies among the migration groups.Type: GrantFiled: August 2, 2019Date of Patent: March 1, 2022Assignee: International Business Machines CorporationInventors: Joel W. Branch, Michael Elton Nidd, Birgit Monika Pfitzmann
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Publication number: 20220044129Abstract: Several aspects are provided for dynamically updating an alert-management system that uses a master ruleset to match alerts in a data processing system with automata for handling the alerts. A method comprises training a machine learning model to correlate the alerts with the automata using a training dataset comprising alerts which were successfully handled by the automata. The machine learning model is then applied to correlate unmatched alerts with the automata, wherein the unmatched alerts were not matched to the automata by the master ruleset. The method further comprises analyzing operation of the machine learning model in relation to correlation of the unmatched alerts to define a new ruleset for matching the unmatched alerts with the automata and outputting the new ruleset for auditing of each rule in the new ruleset. In response to approval of an audited rule, the audited rule is added to the master ruleset.Type: ApplicationFiled: August 6, 2020Publication date: February 10, 2022Inventors: Michael Elton Nidd, Hagen Völzer, Ioana Giurgiu, Jinho Hwang, Larisa Shwartz
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Publication number: 20220035692Abstract: An approach to recommending corrective action to computing system event errors. The approach may include generating a textual description of an event error. The approach may include transforming the textual description into feature vectors with a domain-specific word embedding module. The approach may also include generating a recommendation to correct the event error based on an analysis of the feature vectors. Additionally, the recommendation may be presented for verification.Type: ApplicationFiled: August 3, 2020Publication date: February 3, 2022Inventors: Qing Wang, Larisa Shwartz, Jinho Hwang, Srinivasan Parthasarathy, Michael Elton Nidd, Frank Bagehorn, Jakub Krchák, Tomás Ondrej, Altynbek Orumbayev, Michal Mýlek, Ota Sandr
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Publication number: 20220027331Abstract: A computer-implemented method of cross-environment event correlation includes determining one or more correlated events about an issue across a plurality of domains. A knowledge data is extracted from the issue determined from the one or more correlated events is performed. A correlation graph is generated from the extracted knowledge to trace the issue and group the correlated events into one or more event groups to represent their relationship with the issue. A logical reasoning description is constructed based on the generated correlation graph for a domain-space exploration related to how the issue in one domain affects another domain of the plurality of domains. The one or more event groups of correlated events is provided with an explanation about a cause of the issue based on the logical reasoning description.Type: ApplicationFiled: July 23, 2020Publication date: January 27, 2022Inventors: Jinho Hwang, Larisa Shwartz, Srinivasan Parthasarathy, Qing Wang, Raghuram Srinivasan, Gene L. Brown, Michael Elton Nidd, Frank Bagehorn, Jakub Krchák, Ota Sandr, Tomáš Ondrej, Michal Mýlek, Altynbek Orumbayev
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Patent number: 11221908Abstract: Systems, computer-implemented methods, and computer program products to facilitate discovery of an inexplicit link between a change and an incident in a computing environment are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an analysis component that determines a defined link strength corresponding to links between change data and incident data in a computing environment. The computer executable components further comprise an extraction component that employs a model to identify an inexplicit link between the change data and the incident data based on the defined link strength.Type: GrantFiled: March 2, 2021Date of Patent: January 11, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Raghav Batta, George E. Stark, Maja Vukovic, Alexandre Francisco Da Silva, Jinho Hwang, Michael Elton Nidd, Larisa Shwartz
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Publication number: 20210373861Abstract: A computer implemented method for identifying an application topology includes identifying a sandbox environment corresponding to an application of interest, analyzing the sandbox environment to identify a set of communication links between services within the sandbox environment indicating a first topology, identifying a production system corresponding to the application of interest, querying the production system to identify a set of structural dependencies indicating a second topology, and creating a complete topology of the cloud application by combining the first topology and the second topology. A computer program product and computer system for identifying an application topology are additionally disclosed herein.Type: ApplicationFiled: May 26, 2020Publication date: December 2, 2021Inventors: Srinivasan Parthasarathy, Jinho HWANG, Qing Wang, Larisa Shwartz, Michael Elton Nidd, Frank Bagehorn, Jakub Krchák, Altynbek Orumbayev, Michal Mýlek, Ota Sandr, Tomás Ondrej
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Publication number: 20210350253Abstract: A method, a computer system, and a computer program product for mapping operational records to a topology graph. Embodiments of the present invention may include generating an event frequent pattern using operational records. Embodiments of the present invention may include integrating topology-based event frequent patterns. Embodiments of the present invention may include mapping the operational records with an embedding engine. Embodiments of the present invention may include predicting incident events. Embodiments of the present invention may include receiving labeled patterns to the embedding engine for an active learning cycle.Type: ApplicationFiled: May 8, 2020Publication date: November 11, 2021Inventors: Qing Wang, Larisa Shwartz, Srinivasan Parthasarathy, Jinho HWANG, Tengfei Ma, Michael Elton Nidd, Frank Bagehorn, Jakub Krchák, Altynbek Orumbayev, Michal Mýlek, Ota Sandr, Tomás Ondrej
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Publication number: 20210133622Abstract: The invention relates to a computer-implemented method for processing events. The method provides a database comprising original event objects stored in association with canonical event objects. The method executes a learning algorithm on the associated original and canonical event objects for generating a trained ML program adapted to transform an original event object of any one of the one or more original data formats into a canonical event object having the canonical data format and uses the trained machine learning program for automatically transforming original event objects generated by an active IT-monitoring system into canonical event objects processable by an event handling system.Type: ApplicationFiled: October 31, 2019Publication date: May 6, 2021Inventors: Michael Elton Nidd, Hagen Völzer, Sander Plug, Larisa Shwartz