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1.发明公开公开(公告)号：US20230267033A1
公开(公告)日：2023-08-24
申请号：US17652099
申请日：2022-02-23
Applicant: Healtech Software India Pvt. Ltd.
Inventor： Atri Mandal , Palavali Shravan Kumar Reddy , Sudhir Shetty , Adityam Ghosh , Shainy Merin , Raja Shekhar Mulpuri , Howard Zhang
CPC classification number: G06F11/0793 , G06F11/079 , G06F11/3409 , G06N5/02
Abstract: According to an aspect, a (recommendation) system constructs a knowledge graph based on problem descriptors and remediation actions contained in multiple incident reports previously received from a performance management (PM) system. Each problem descriptor and remediation action in an incident report are represented as corresponding start node and end node in the knowledge graph, with a set of qualifier entities in the incident report represented as causal links between the start node and the end node. Upon receiving an incident report related to an incident identified by the PM system, the system extracts a problem descriptor and a set of qualifier entities. The system traverses the knowledge graph starting from a start node corresponding to the extracted problem descriptor using the set of qualifier entities to determine end nodes representing a set of remediation actions. The system provides the set of remediation actions as recommendations for resolving the incident.
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公开(公告)号：US11899553B2
公开(公告)日：2024-02-13
申请号：US17645754
申请日：2021-12-23
Applicant: Healtech Software India Pvt. Ltd.
Inventor： Atri Mandal , Sudhir Shetty , Jaisri S , Palavali Shravan Kumar Reddy
CPC classification number: G06F11/3419 , G06F11/3072 , G06F11/3452 , G06F11/3466 , G06N20/00
Abstract: An aspect of the present disclosure provides a relevance ranking system for events identified by performance management systems. In one embodiment, a system receives, from one or more performance management systems, events associated with performance metrics and determines, based on the received events, a corresponding relevance score for each performance metric. The system generates a ranking list of the received events based on the corresponding relevance scores (determined for the performance metrics). The system then provides the ranking list of events to a user (such as an administrator). According to another aspect, the system (noted above) performs the steps of receiving, determining, and generating for each time interval of a set of time intervals.
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3.发明授权公开(公告)号：US11860726B2
公开(公告)日：2024-01-02
申请号：US17652099
申请日：2022-02-23
Applicant: Healtech Software India Pvt. Ltd.
Inventor： Atri Mandal , Palavali Shravan Kumar Reddy , Sudhir Shetty , Adityam Ghosh , Shainy Merin , Raja Shekhar Mulpuri , Howard Zhang
CPC classification number: G06F11/0793 , G06F11/079 , G06F11/3409 , G06N5/02
Abstract: According to an aspect, a (recommendation) system constructs a knowledge graph based on problem descriptors and remediation actions contained in multiple incident reports previously received from a performance management (PM) system. Each problem descriptor and remediation action in an incident report are represented as corresponding start node and end node in the knowledge graph, with a set of qualifier entities in the incident report represented as causal links between the start node and the end node. Upon receiving an incident report related to an incident identified by the PM system, the system extracts a problem descriptor and a set of qualifier entities. The system traverses the knowledge graph starting from a start node corresponding to the extracted problem descriptor using the set of qualifier entities to determine end nodes representing a set of remediation actions. The system provides the set of remediation actions as recommendations for resolving the incident.
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公开(公告)号：US12164841B2
公开(公告)日：2024-12-10
申请号：US17447448
申请日：2021-09-13
Applicant: Healtech Software India Pvt. Ltd.
Inventor： Atri Mandal , Raja Shekhar Mulpuri
IPC: G06F30/20 , G06N20/00 , G06Q10/0639 , G06F3/14
Abstract: An aspect of the present disclosure facilitates measuring the capability of AIOps (Artificial Intelligence for IT operations) systems deployed in computing environments. In one embodiment, a first simulation of a target AIOps system is run using a first historical input set having a corresponding first actual output set of a first AIOps system different from the target AIOps system. A second simulation of a reference AIOps system is run using a second historical input set having a corresponding second actual output set of the same first AIOps system. A first and second accuracy scores are determined based on outputs of the first and second simulations and the corresponding first and second actual output sets. An enablement score representing a measure of the capability (in terms of accuracy of prediction) of the target AIOps system is generated based on the first accuracy score and the second accuracy score.
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Patent Agency Ranking
公开(公告)号：US20230008225A1
公开(公告)日：2023-01-12
申请号：US17447448
申请日：2021-09-13
Applicant: Healtech Software India Pvt. Ltd.
Inventor： Atri Mandal , Raja Shekhar Mulpuri
Abstract: An aspect of the present disclosure facilitates measuring the capability of AIOps (Artificial Intelligence for IT operations) systems deployed in computing environments. In one embodiment, a first simulation of a target AIOps system is run using a first historical input set having a corresponding first actual output set of a first AIOps system different from the target AIOps system. A second simulation of a reference AIOps system is run using a second historical input set having a corresponding second actual output set of the same first AIOps system. A first and second accuracy scores are determined based on outputs of the first and second simulations and the corresponding first and second actual output sets. An enablement score representing a measure of the capability (in terms of accuracy of prediction) of the target AIOps system is generated based on the first accuracy score and the second accuracy score.
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公开(公告)号：US12130720B2
公开(公告)日：2024-10-29
申请号：US17664050
申请日：2022-05-19
Applicant: Healtech Software India Pvt. Ltd.
Inventor： Atri Mandal , Sudhir Shetty , Arpit Rathi
CPC classification number: G06F11/3476 , G06F11/079 , G06F11/3409 , G06N7/01 , G06N7/02 , G06F11/0793 , G06F11/3447
Abstract: Proactive avoidance of performance issues in computing environments. In one embodiment, a causal dependency graph representing the usage dependencies among the various components of a computing environment is formed, the components being associated with key performance indicators (KPIs). A probabilistic model is trained with prior incidents that have occurred in the components to correlate outliers of KPIs in associated components to prior incidents. The training includes determining the correlation based on the causal dependency graph. Upon detecting the occurrence of outliers for performance metrics, an imminent performance issue likely to occur in a specific component is identified based on the probabilistic model and the detected outliers. A preventive action is performed to avoid the occurrence of the imminent performance issue in the specific component.
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公开(公告)号：US20230281099A1
公开(公告)日：2023-09-07
申请号：US18179402
申请日：2023-03-07
Applicant: Healtech Software India Pvt. Ltd.
Inventor： Raja Shekhar Mulpuri , Jaisri S , Rashmi MS
CPC classification number: G06F11/3409 , G06F11/0769 , G06F11/0721
Abstract: According to an aspect, metrics of an application are measured, the measured metrics including a first metric and a second metric, the first metric representing a measure of the responsiveness of the application when processing user requests received from users, and the second metric representing a measure of the responsiveness to resolution of problems identified in the application. A single number is calculated based on the metrics including the first metric and the second metric. The calculated single number is provided (to a user) as representing a user impact index for the application. Thus, the user impact index provided captures both the responsiveness of the application when processing user requests and the responsiveness to resolution of problems identified in the application.
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公开(公告)号：US20230176920A1
公开(公告)日：2023-06-08
申请号：US18062033
申请日：2022-12-06
Applicant: Healtech Software India Pvt. Ltd.
Inventor： Raja Shekhar Mulpuri , Atri Mandal , Palavali Shravan Kumar Reddy , Jaisri S , Adityam Ghosh
IPC: G06F9/50
CPC classification number: G06F9/5055 , G06F9/5038 , G06F2209/5019
Abstract: An aspect of the present disclosure is directed to forecasting resource requirements for components of software applications. In one embodiment, a system constructs a component graph of components deployed in a computing environment, the component graph indicating for each component, a corresponding subset of components that are invoked by the component and a corresponding distribution of component workloads received at the component to the subset of components. Upon receiving data indicating an entry workload expected to be received in a future duration at one or more entry components, the system estimates by traversing the component graph, a component workload, corresponding to the entry workload, expected to be received in the future duration at a first component and determines resource requirements for the first component based on the estimated component workload.
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公开(公告)号：US20220374810A1
公开(公告)日：2022-11-24
申请号：US17444673
申请日：2021-08-09
Applicant: Healtech Software India Pvt. Ltd.
Inventor： Atri Mandal , Arpit Rathi , Raja Shekhar Mulpuri , Vihang Dudhalkar
Abstract: An aspect of the present disclosure facilitates accelerating outlier prediction of performance metrics in performance managers deployed in new computing environments. In one embodiment, a digital processing system receives an input data specifying a business vertical to which a new computing environment is directed, a performance metric of interest, and a computing component of the new computing environment for which the performance metric is sought to be measured. In response, the system selects, from a set of prediction models, a prediction model for the performance metric, based on the input data. The selected prediction model is then used in a performance manager to predict outliers for the performance metric of interest during operation of the new computing environment.
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