Patents by Inventor Erhan Giral

Erhan Giral 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).

  • Publication number: 20240095117
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Application
    Filed: November 27, 2023
    Publication date: March 21, 2024
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Patent number: 11892904
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Grant
    Filed: March 31, 2022
    Date of Patent: February 6, 2024
    Assignee: BMC Software, Inc.
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Patent number: 11874732
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Grant
    Filed: March 31, 2022
    Date of Patent: January 16, 2024
    Assignee: BMC Software, Inc.
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Patent number: 11734101
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Grant
    Filed: March 31, 2022
    Date of Patent: August 22, 2023
    Assignee: BMC Software, Inc.
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Publication number: 20230214693
    Abstract: Described systems and techniques perform causal chain extraction for an investigated event in a system, using a neural network trained to represent a temporalsequence of events within the system. Such neural networks, by themselves, may be successful in predicting or characterizing system events, without providing useful interpretations of causation between the system events. Described techniques use the representational nature of neural networks to perform intervention testing using the neural network, distinguish confounding events, and identify a probabilistic root cause of the investigated event.
    Type: Application
    Filed: December 31, 2021
    Publication date: July 6, 2023
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Publication number: 20230122406
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Application
    Filed: March 31, 2022
    Publication date: April 20, 2023
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Publication number: 20230102786
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, continuously generate a knowledge graph, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Application
    Filed: September 23, 2022
    Publication date: March 30, 2023
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Publication number: 20230095270
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Application
    Filed: March 31, 2022
    Publication date: March 30, 2023
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Publication number: 20230098896
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Application
    Filed: March 31, 2022
    Publication date: March 30, 2023
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Publication number: 20230096290
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Application
    Filed: March 31, 2022
    Publication date: March 30, 2023
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Publication number: 20230102002
    Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
    Type: Application
    Filed: March 31, 2022
    Publication date: March 30, 2023
    Inventors: Sai Eswar Garapati, Erhan Giral
  • Patent number: 11068300
    Abstract: A topology-based transversal analysis service has been created that correlates topologies of different domains of a distributed application and creates cross-domain “stories” for the different types of transactions provided by the distributed application. A “story” for a transaction type associates an event(s) with a node in an execution path of the transaction type. This provides context to the event(s) with respect to the transaction type (“transaction contextualization”) and their potential business impact. The story is a journal of previously detected events and/or information based on previously detected events. The events have been detected over multiple instances of a transaction type and the journal is contextualized within an aggregate of execution paths of the multiple instances of the transaction type. The story can be considered a computed, ongoing narrative around application and infrastructure performance events, and the narrative grows as more performance-related events are detected.
    Type: Grant
    Filed: August 5, 2019
    Date of Patent: July 20, 2021
    Assignee: CA, Inc.
    Inventors: Erhan Giral, Tomas Kolda
  • Patent number: 11037033
    Abstract: A multivariate clustering-based anomaly detector can generate an event for consumption by an APM manager that indicates detection of an anomaly based on multivariate clustering analysis after topology-based feature selection. The anomaly detector accumulates time-series data across a series of time instants to form a multivariate time-series data slice or multivariate data slice. The anomaly detector then performs multivariate clustering analysis with the multivariate data slice. The anomaly detector determines whether a multivariate data slice is within a cluster of multivariate data slices. If the multivariate data slice is within the cluster and the cluster is a known anomaly cluster, then the anomaly detector generates an anomaly detection event indicating detection of the known anomaly. The anomaly detector can also determine that a multivariate data slice is within an unknown cluster and generate an event indicating detection of an unknown anomaly.
    Type: Grant
    Filed: March 29, 2018
    Date of Patent: June 15, 2021
    Assignee: CA, Inc.
    Inventors: Smrati Gupta, Erhan Giral, David Sanchez Charles, Victor Muntés-Mulero
  • Patent number: 10831809
    Abstract: Large amounts of data from user interactions with web resources is available as data logs. Analysis may be performed to process the data log in order to determine the characteristics of the user interactions. Data log analysis may include identifying page states, which may be sets of frequent attributes and values that occur together in a session. The data log analysis may also include generating semantic labels of page states, which may describe the function of pages corresponding to different page states. Text mining models may be used to determine the semantic labels. Analysis may also include aggregating sets of page paths to create page journeys. These page journeys may be aggregated over all users, all user sessions, or other subsets of the clickstream. In some embodiments, comparing page journeys may provide recommendations for potential methods to improve the site and enhance user experiences.
    Type: Grant
    Filed: August 31, 2017
    Date of Patent: November 10, 2020
    Assignee: CA TECHNOLOGIES, INC.
    Inventors: Cui Lin, Erhan Giral, Corey Cohen
  • Patent number: 10678831
    Abstract: Large amounts of data from user interactions with web resources is available as data logs. Analysis may be performed to process the data log in order to determine the characteristics of the user interactions. Data log analysis may include identifying page states, which may be sets of frequent attributes and values that occur together in a session. The data log analysis may also include generating semantic labels of page states, which may describe the function of pages corresponding to different page states. Text mining models may be used to determine the semantic labels. Analysis may also include aggregating sets of page paths to create page journeys. These page journeys may be aggregated over all users, all user sessions, or other subsets of the clickstream. In some embodiments, comparing page journeys may provide recommendations for potential methods to improve the site and enhance user experiences.
    Type: Grant
    Filed: August 31, 2017
    Date of Patent: June 9, 2020
    Assignee: CA TECHNOLOGIES, INC.
    Inventors: Cui Lin, Erhan Giral, Corey Cohen
  • Patent number: 10671471
    Abstract: Instead of attempting to scan all metric measurements of a distributed application, an anomaly detector intelligently selects instances of metrics from the universe of metric instances available for the distributed application to detect anomalies. Intelligent feature selection allows the anomaly detector to efficiently and reliably detect anomalies for a distributed application. The intelligent selection is guided by execution paths of transactions of the distributed application, and the execution paths are determined from a topology of the distributed application. The anomaly detector scans the incoming time-series data of the selected metric instances by transaction type and determines whether the scanned measurements across the selected metric instances form a pattern correlated with anomalous behavior.
    Type: Grant
    Filed: February 12, 2018
    Date of Patent: June 2, 2020
    Assignee: CA, Inc.
    Inventors: Smrati Gupta, Erhan Giral
  • Patent number: 10628289
    Abstract: A multivariate path-based anomaly detection and prediction service (“anomaly detector”) can generate a prediction event for consumption by the APM manager that indicates a likelihood of an anomaly occurring based on path analysis of multivariate values after topology-based feature selection. To predict that a set of metrics will travel to a cluster that represents anomalous application behavior, the anomaly detector analyzes a set of multivariate date slices that are not within a cluster to determine whether dimensionally reduced representations of the set of multivariate data slices fit a path as described by a function.
    Type: Grant
    Filed: March 29, 2018
    Date of Patent: April 21, 2020
    Assignee: CA, Inc.
    Inventors: Smrati Gupta, Erhan Giral, David Sanchez Charles, Victor Muntés-Mulero
  • Publication number: 20200110841
    Abstract: To extract meaningful information that aids in analysis of a web application or web site based on page summarizations without impractical resource demand, statistical modeling is employed to approximately identify pages across web application transactions and predict meaningful content or items of information within the pages. Statistics are collected on a sample of traffic for a web application. The collected statistics are on tokens generated from messages that correspond to web pages. Statistics are collected by message, by transaction, and across the sampling of messages. Descriptive tokens that meaningfully describe a web page and attribute-value pair tokens are scored. Those of the tokens that satisfy selection criteria are selected as a basis for generating extraction rules. Subsequently, the extraction rules are applied to message payloads to efficiently extract descriptive “tags” and attribute-value pairs.
    Type: Application
    Filed: October 9, 2018
    Publication date: April 9, 2020
    Inventors: Corey Adam Cohen, Erhan Giral
  • Publication number: 20200074306
    Abstract: A genetic algorithm (GA) in combination with a random decision forest can be used to identify a feature subset related to an observed incident. The GA is used to select feature subsets for which data samples are obtained to train and test random decision forests per individual feature subset (“individual”) with respect to an observed incident. For each generation of a GA run, fitness values of the individuals are determined based on the testing of the corresponding random decision forest. At termination of the GA run, an individual representing a feature subset is identified as likely most related to the observed incident. The trained random decision forest corresponding to the individual or a subset of the trained random decision forest is used to predict or classify whether live values of the fittest feature subset indicate the observed incident.
    Type: Application
    Filed: August 31, 2018
    Publication date: March 5, 2020
    Inventors: Erhan Giral, Thomas Patrick Kennedy, Mark Jacob Addleman, Nathan Allan Isley, Michael J. Cohen
  • Publication number: 20190354394
    Abstract: A topology-based transversal analysis service has been created that correlates topologies of different domains of a distributed application and creates cross-domain “stories” for the different types of transactions provided by the distributed application. A “story” for a transaction type associates an event(s) with a node in an execution path of the transaction type. This provides context to the event(s) with respect to the transaction type (“transaction contextualization”) and their potential business impact. The story is a journal of previously detected events and/or information based on previously detected events. The events have been detected over multiple instances of a transaction type and the journal is contextualized within an aggregate of execution paths of the multiple instances of the transaction type. The story can be considered a computed, ongoing narrative around application and infrastructure performance events, and the narrative grows as more performance-related events are detected.
    Type: Application
    Filed: August 5, 2019
    Publication date: November 21, 2019
    Inventors: Erhan Giral, Tomas Kolda