Patents by Inventor Philipp REINECKE

Philipp REINECKE 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).

  • Patent number: 10572672
    Abstract: An apparatus comprises a memory to store data and a processor coupled to the memory. The processor may modify a plurality of data elements using a semantic relationship between the plurality of data elements and a pre-selected data security policy and to store data representing the modified plurality of data elements in the memory.
    Type: Grant
    Filed: August 14, 2015
    Date of Patent: February 25, 2020
    Assignee: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
    Inventors: Adrian John Baldwin, Patrick Goldsack, Brian Quentin Monahan, Philipp Reinecke
  • Patent number: 10331528
    Abstract: Example implementations relate to capturing and/or recovering components of a computing system. A recovery service may receive a recovery script from an external recovery script repository, wherein the recovery script may include a number of actions, each respective action being a capture action or a recovery action. For action in the recovery script, the recovery service may request a recovery agent to perform the action on a component of the computing system.
    Type: Grant
    Filed: March 2, 2017
    Date of Patent: June 25, 2019
    Assignee: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
    Inventors: Kate Elizabeth Reinecke, Philipp Reinecke, Stephen James Crane
  • Patent number: 10310877
    Abstract: Examples analyze source code of a task prior to compiling the source code to determine a static property of the task. Examples determine a category for the task based at least in part on the static property. Examples compile the source code to generate a binary of the task. Examples determine execution parameters for the task based at least in part on the category. Examples schedule the binary for execution based at least in part on the execution parameters.
    Type: Grant
    Filed: July 31, 2015
    Date of Patent: June 4, 2019
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Philipp Reinecke, Brian Quentin Monahan, Granville Barnett, Patrick Goldsack
  • Patent number: 10262132
    Abstract: Examples relate to model-based computer attack analytics orchestration. In one example, a computing device may: generate, using an attack model that specifies behavior of a particular attack on a computing system, a hypothesis for the particular attack, the hypothesis specifying, for a particular state of the particular attack, at least one attack action; identify, using the hypothesis, at least one analytics function for determining whether the at least one attack action specified by the hypothesis occurred on the computing system; provide an analytics device with instructions to execute the at least one analytics function on the computing system; receive analytics results from the analytics device; and update a state of the attack model based on the analytics results.
    Type: Grant
    Filed: July 1, 2016
    Date of Patent: April 16, 2019
    Assignee: ENTIT SOFTWARE LLC
    Inventors: Philipp Reinecke, Marco Casassa Mont, Yolanta Beresna
  • Publication number: 20180253362
    Abstract: Example implementations relate to capturing and/or recovering components of a computing system. A recovery service may receive a recovery script from an external recovery script repository, wherein the recovery script may include a number of actions, each respective action being a capture action or a recovery action. For action in the recovery script, the recovery service may request a recovery agent to perform the action on a component of the computing system.
    Type: Application
    Filed: March 2, 2017
    Publication date: September 6, 2018
    Inventors: Kate Elizabeth Reinecke, Philipp Reinecke, Stephen James Crane
  • Publication number: 20180219884
    Abstract: Example implementations relate to changing deployment statuses. An example implementation includes updating a data source data store comprising descriptors of available data sources, a pre-processor data store comprising descriptors of available pre-processors, or an analytic data store comprising descriptors of available analytics. A change request may be initiated responsive to a change in the data source data, pre-processor data, or analytic data and a deployment status of a pre-processor or an analytic may be changed responsive to the change request.
    Type: Application
    Filed: January 27, 2017
    Publication date: August 2, 2018
    Inventors: Yolanta Beresna, Marco Casassa Mont, Philipp Reinecke
  • Publication number: 20180165459
    Abstract: An apparatus comprises a memory to store data and a processor coupled to the memory. The processor may modify a plurality of data elements using a semantic relationship between the plurality of data elements and a pre-selected data security policy and to store data representing the modified plurality of data elements in the memory.
    Type: Application
    Filed: August 14, 2015
    Publication date: June 14, 2018
    Inventors: Adrian John Baldwin, Patrick Goldsack, Brian Quentin Monahan, Philipp Reinecke
  • Publication number: 20180113729
    Abstract: Examples analyze source code of a task prior to compiling the source code to determine a static property of the task. Examples determine a category for the task based at least in part on the static property. Examples compile the source code to generate a binary of the task. Examples determine execution parameters for the task based at least in part on the category. Examples schedule the binary for execution based at least in part on the execution parameters.
    Type: Application
    Filed: July 31, 2015
    Publication date: April 26, 2018
    Inventors: Philipp Reinecke, Brian Quentin Monahan, Granville Barnett, Patrick Goldsack
  • Publication number: 20180004941
    Abstract: Examples relate to model-based computer attack analytics orchestration. In one example, a computing device may: generate, using an attack model that specifies behavior of a particular attack on a computing system, a hypothesis for the particular attack, the hypothesis specifying, for a particular state of the particular attack, at least one attack action; identify, using the hypothesis, at least one analytics function for determining whether the at least one attack action specified by the hypothesis occurred on the computing system; provide an analytics device with instructions to execute the at least one analytics function on the computing system; receive analytics results from the analytics device; and update a state of the attack model based on the analytics results.
    Type: Application
    Filed: July 1, 2016
    Publication date: January 4, 2018
    Inventors: Philipp Reinecke, Marco Casassa Mont, Yolanta Beresna
  • Publication number: 20180004958
    Abstract: Examples relate to computer attack model management. In one example, a computing device may: identify a first set of attack models, each attack model in the first set specifying behavior of a particular attack on a computing system; obtain, for each attack model in the first set, performance data that indicates at least one measure of attack model performance for a previous use of the attack model in determining whether the particular attack occurred on the computing system; and update the first set of attack models based on the performance data.
    Type: Application
    Filed: July 1, 2016
    Publication date: January 4, 2018
    Inventors: Philipp Reinecke, Marco Casassa Mont, Yolanta Beresna
  • Publication number: 20170046629
    Abstract: According to an example, statistics-based data trace classification may include generating sets of training data traces from training data information by assigning a subset of the training data information that has a predetermined property with a first label and assigning another subset of the training data information that does not have the predetermined property with a second label. A trained trace classifier may be generated to detect whether or not a set of input data traces satisfies the predetermined property. The trace classifier may be trained to learn the predetermined property from a statistical data object determined from the sets of the training data traces, and the first and second labels related to the sets of the training data traces.
    Type: Application
    Filed: April 25, 2014
    Publication date: February 16, 2017
    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
    Inventors: Philipp REINECKE, Brian Quentin MONAHAN, Jonathan GRIFFIN