Patents by Inventor Eitan D. Farchi

Eitan D. Farchi 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: 11334816
    Abstract: A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical ontology data structure. A surface finding component executing within the training system selects a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The surface finding component determines a plurality of adjacent surfaces that differ from the selected component by one node. The surface finding component selects an optimal surface, based on the selected surface and the plurality of adjacent surfaces, that maximizes accuracy and coverage. The training system trains a classifier model for a cognitive system using the optimal surface and the training data set.
    Type: Grant
    Filed: November 14, 2018
    Date of Patent: May 17, 2022
    Assignee: International Business Machines Corporation
    Inventors: Eitan D. Farchi, Pathirage Perera, Orna Raz
  • Patent number: 11281995
    Abstract: A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical ontology data structure. A surface finding component executing within the training system selects a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The surface finding component determines a plurality of adjacent surfaces that differ from the selected component by one node. The surface finding component selects an optimal surface, based on the selected surface and the plurality of adjacent surfaces, that maximizes accuracy and coverage. The training system trains a classifier model for a cognitive system using the optimal surface and the training data set.
    Type: Grant
    Filed: May 21, 2018
    Date of Patent: March 22, 2022
    Assignee: International Business Machines Corporation
    Inventors: Eitan D. Farchi, Pathirage Perera, Orna Raz
  • Publication number: 20200394461
    Abstract: A computer system trains a machine learning model. A vector representation is generated for each document in a collection of documents. The documents are clustered based on the vector representations of the documents to produce a plurality of clusters. A training set is produced by selecting one or more documents from each cluster, wherein the selected documents represent a sample of the collection of documents to train the machine learning model. The machine learning model is trained by applying the training set to the machine learning model. Embodiments of the present invention further include a method and program product for training a machine learning model in substantially the same manner described above.
    Type: Application
    Filed: June 12, 2019
    Publication date: December 17, 2020
    Inventors: Pathirage D. S. U. Perera, Eitan D. Farchi, Orna Raz, Ramani Routray, Sheng Hua Bao, Marcel Zalmanovici
  • Publication number: 20200285943
    Abstract: A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical classification ontology data structure. The training system generates a neural network architecture based on the training data set and the hierarchical classification ontology data structure. The neural network architecture comprises an indicative layer, a parent tier (PT) output and a lower leaf tier (LLT) output. The training system trains the neural network architecture to classify the training data set to leaf nodes at the LLT output and parent nodes at the PT output. The indicative layer in the neural network architecture determines a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure.
    Type: Application
    Filed: March 4, 2019
    Publication date: September 10, 2020
    Inventors: Pathirage Dinindu Sujan Udayanga Perera, Orna Raz, Ramani Routray, Vivek Krishnamurthy, Sheng Hua Bao, Eitan D. Farchi
  • Publication number: 20190354899
    Abstract: A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical ontology data structure. A surface finding component executing within the training system selects a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The surface finding component determines a plurality of adjacent surfaces that differ from the selected component by one node. The surface finding component selects an optimal surface, based on the selected surface and the plurality of adjacent surfaces, that maximizes accuracy and coverage. The training system trains a classifier model for a cognitive system using the optimal surface and the training data set.
    Type: Application
    Filed: November 14, 2018
    Publication date: November 21, 2019
    Inventors: Eitan D. Farchi, Pathirage Perera, Orna Raz
  • Publication number: 20190354898
    Abstract: A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical ontology data structure. A surface finding component executing within the training system selects a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The surface finding component determines a plurality of adjacent surfaces that differ from the selected component by one node. The surface finding component selects an optimal surface, based on the selected surface and the plurality of adjacent surfaces, that maximizes accuracy and coverage. The training system trains a classifier model for a cognitive system using the optimal surface and the training data set.
    Type: Application
    Filed: May 21, 2018
    Publication date: November 21, 2019
    Inventors: Eitan D. Farchi, Pathirage Perera, Orna Raz
  • Patent number: 9734329
    Abstract: Mitigating return-oriented programming attacks. From program code and associated components needed by the program code for execution, machine language instruction sequences that may be combined and executed as malicious code are selected. A predetermined number of additional copies of each of the selected machine language instruction sequences are made, and the additional copies are marked as non-executable. The machine language instruction sequences and the non-executable copies are distributed in memory. If a process attempts to execute a machine language instruction sequence that has been marked non-executable, the computer may initiate protective action.
    Type: Grant
    Filed: April 19, 2016
    Date of Patent: August 15, 2017
    Assignee: International Business Machines Corporation
    Inventors: Omer Y. Boehm, Eitan D. Farchi, Oded Margalit, Yousef Shajrawi, Michael Vinov
  • Patent number: 9690553
    Abstract: Embodiments include method, systems and computer program products for identifying dependency relationships in a software product. Aspects include obtaining change history data for the software product and extracting a plurality of change elements from the change history data, each change element including an identifier of a code segment that was changed and a timestamp of the change. Aspects also include creating a dependency graph based on the plurality of change elements, wherein the dependency graph includes nodes that correspond to the code segments and edges that connect nodes that were both updated in a same logical grouping, calculating a weight for each of the edges based on probability that the nodes connected by the edge will be updated together, and outputting the dependency graph.
    Type: Grant
    Filed: September 26, 2016
    Date of Patent: June 27, 2017
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Aharon Brodie, Eitan D. Farchi, Michael E. Gildein, II, Sergey Novikov, Richard D. Prewitt, Jr., Orna Raz-Pelleg
  • Patent number: 9665717
    Abstract: Mitigating return-oriented programming (ROP) attacks. Program code and associated components are received and loaded into memory. From the program code and associated components, a predetermined number of sequences of machine language instructions that terminate in a return instruction are selected. The sequences of machine language instructions include: machine language instruction sequences that are equivalent to a conditional statement “if-then-else return,” sequences of machine language instructions corresponding to known malicious code sequences, and sequences of machine language instructions corresponding to machine language instructions in known toolkits for assembling malicious code sequences.
    Type: Grant
    Filed: September 13, 2016
    Date of Patent: May 30, 2017
    Assignee: International Business Machines Corporation
    Inventors: Omer Y. Boehm, Eitan D. Farchi, Oded Margalit, Yousef Shajrawi, Michael Vinov
  • Patent number: 9665710
    Abstract: Mitigating return-oriented programming attacks. Program code and associated components are received and loaded into memory. From the program code and associated components, a predetermined number of sequences of machine language instructions that terminate in a return instruction are selected. The sequences of machine language instructions include: machine language instruction sequences that are equivalent to a conditional statement “if-then-else return,” sequences of machine language instructions corresponding to known malicious code sequences, and sequences of machine language instructions corresponding to machine language instructions in known toolkits for assembling malicious code sequences.
    Type: Grant
    Filed: September 14, 2016
    Date of Patent: May 30, 2017
    Assignee: International Business Machines Corporation
    Inventors: Omer Y. Boehm, Eitan D. Farchi, Oded Margalit, Yousef Shajrawi, Michael Vinov
  • Publication number: 20170091456
    Abstract: Mitigating return-oriented programming (ROP) attacks. Program code and associated components are received and loaded into memory. From the program code and associated components, a predetermined number of sequences of machine language instructions that terminate in a return instruction are selected. The sequences of machine language instructions include: machine language instruction sequences that are equivalent to a conditional statement “if-then-else return,” sequences of machine language instructions corresponding to known malicious code sequences, and sequences of machine language instructions corresponding to machine language instructions in known toolkits for assembling malicious code sequences.
    Type: Application
    Filed: September 13, 2016
    Publication date: March 30, 2017
    Inventors: Omer Y. Boehm, Eitan D. Farchi, Oded Margalit, Yousef Shajrawi, Michael Vinov
  • Publication number: 20170091449
    Abstract: Mitigating return-oriented programming attacks. From program code and associated components needed by the program code for execution, machine language instruction sequences that may be combined and executed as malicious code are selected. A predetermined number of additional copies of each of the selected machine language instruction sequences are made, and the additional copies are marked as non-executable. The machine language instruction sequences and the non-executable copies are distributed in memory. If a process attempts to execute a machine language instruction sequence that has been marked non-executable, the computer may initiate protective action.
    Type: Application
    Filed: April 19, 2016
    Publication date: March 30, 2017
    Inventors: Omer Y. Boehm, Eitan D. Farchi, Oded Margalit, Yousef Shajrawi, Michael Vinov
  • Publication number: 20170091447
    Abstract: Mitigating return-oriented programming attacks. Program code and associated components are received and loaded into memory. From the program code and associated components, a predetermined number of sequences of machine language instructions that terminate in a return instruction are selected. The sequences of machine language instructions include: machine language instruction sequences that are equivalent to a conditional statement “if-then-else return,” sequences of machine language instructions corresponding to known malicious code sequences, and sequences of machine language instructions corresponding to machine language instructions in known toolkits for assembling malicious code sequences.
    Type: Application
    Filed: September 14, 2016
    Publication date: March 30, 2017
    Inventors: Omer Y. Boehm, Eitan D. Farchi, Oded Margalit, Yousef Shajrawi, Michael Vinov
  • Patent number: 9576138
    Abstract: Mitigating return-oriented programming attacks. From program code and associated components needed by the program code for execution, machine language instruction sequences that may be combined and executed as malicious code are selected. A predetermined number of additional copies of each of the selected machine language instruction sequences are made, and the additional copies are marked as non-executable. The machine language instruction sequences and the non-executable copies are distributed in memory. If a process attempts to execute a machine language instruction sequence that has been marked non-executable, the computer may initiate protective action.
    Type: Grant
    Filed: September 30, 2015
    Date of Patent: February 21, 2017
    Assignee: International Business Machines Corporation
    Inventors: Omer Y. Boehm, Eitan D. Farchi, Oded Margalit, Yousef Shajrawi, Michael Vinov
  • Patent number: 9244819
    Abstract: A method for modeling a test space is provided. The method comprises defining a coverage model including one or more attributes, wherein respective values for the attributes are assigned, one or more definitions of value properties for said attributes with assigned values, and one or more requirements that limit combination of attribute values that are legal for the model, wherein at least one of said requirements is defined using at least one value property.
    Type: Grant
    Filed: April 10, 2013
    Date of Patent: January 26, 2016
    Assignee: International Business Machines Corporation
    Inventors: Eitan D Farchi, Howard Hess, Itai Segall, Rachel Tzoref-Brill, Aviad Zlotnick
  • Patent number: 9189372
    Abstract: A method and apparatus for estimating coverage of a computer program from traces, the method comprising: receiving trace data generated by an execution of an executable unit; and estimating coverage of the executable unit from the trace data, wherein estimating coverage comprises estimating trace coverage.
    Type: Grant
    Filed: March 11, 2013
    Date of Patent: November 17, 2015
    Assignee: International Business Machines Corporation
    Inventors: Yoram S. Adler, Eitan D. Farchi, Orna Raz-Pelleg
  • Publication number: 20150254167
    Abstract: A computer-implemented method performed by a computerized device, apparatus and computer program product, the method comprising: displaying to a user two or more options related to one or more principle attributes of a test; receiving from the user selection for an option related to a principle attribute; receiving from the user a value for the principle attribute; and generating a description of the test, the description comprising the value for the principle attribute.
    Type: Application
    Filed: March 10, 2014
    Publication date: September 10, 2015
    Applicant: International Business Machines Corporation
    Inventors: Eitan D. Farchi, Aviad Zlotnick
  • Publication number: 20150212993
    Abstract: A method that includes obtaining an area of text that has changed in a functional document, wherein the functional document corresponds to one or more coverage tasks. The method also includes computing an impact measurement for each of the one or more coverage task, wherein the impact measurement is indicative of a potential to be impacted by the change. As a result of the method it is possible to identify coverage tasks that are estimated to be impacted by the change of the functional document.
    Type: Application
    Filed: January 30, 2014
    Publication date: July 30, 2015
    Applicant: International Business Machines Corporation
    Inventors: Eitan D. Farchi, Mircea Namolaru, Orna Raz-Pelleg
  • Publication number: 20150106653
    Abstract: Method, apparatus and product for test selection based on domination criterion. In some embodiments, excluding from a test suite dominated tests, each of which is dominated by a predetermined number of dominating tests, wherein a dominated test is dominated by a dominating test if each target that is covered by the dominated test is also covered by the dominating test. In some embodiments, a reduced test suite is determined by excluding from a test suite each test that covers a dominated set of targets that is N-dominated by the reduced test suite, wherein a dominated set of targets is N-dominated by a set of tests if each target in the dominated set of targets is covered by at least N tests in the set of tests, wherein N is a predetermined number greater than one.
    Type: Application
    Filed: October 10, 2013
    Publication date: April 16, 2015
    Applicant: International Business Machines Corporation
    Inventors: Yoram S. Adler, Dale E. Blue, Eitan D. Farchi, Orna Raz-Pelleg, Aviad Zlotnick
  • Patent number: 8990626
    Abstract: An apparatus and computer-implemented method for determining relevance of assignments in combinatorial models, the method comprising: receiving an attribute collection, the attribute collection comprising one or more attributes and one or more possible values for each of attributes; receiving pone or more restrictions, each restriction indicating one or more values for one or more attributes; receiving one or more assignments comprising one or more assigned values for one or more of the attributes; and determining whether the assignment is legal, illegal or partially-legal, wherein an illegal assignment is an assignment which violates a constraint by itself; a legal assignment is an assignment which is not illegal, and for every extension thereof which is illegal, a combination of values assigned to other attributes violates a constraint by itself; and a partially-legal assignment is an assignment which is neither legal nor illegal.
    Type: Grant
    Filed: December 17, 2012
    Date of Patent: March 24, 2015
    Assignee: International Business Machines Corporation
    Inventors: Eitan D Farchi, Itai Segall, Rachel Yosef Tzoref-Brill