Patents by Inventor Moises Goldszmidt

Moises Goldszmidt 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: 8078913
    Abstract: Methods for automatically identifying and classifying a crisis state occurring in a system having a plurality of computer resources. Signals are received from a device that collects the signals from each computer resource in the system. For each epoch, an epoch fingerprint is generated. Upon detecting a performance crisis within the system, a crisis fingerprint is generated consisting of at least one epoch fingerprint. The technology is able to identify that a performance crisis has previously occurred within the datacenter if a generated crisis fingerprint favorably matches any of the model crisis fingerprints stored in a database. The technology may also predict that a crisis is about to occur.
    Type: Grant
    Filed: May 28, 2009
    Date of Patent: December 13, 2011
    Assignee: Microsoft Corporation
    Inventors: Moises Goldszmidt, Peter Bodik
  • Patent number: 8010341
    Abstract: Mechanisms are disclosed for incorporating prototype information into probabilistic models for automated information processing, mining, and knowledge discovery. Examples of these models include Hidden Markov Models (HMMs), Latent Dirichlet Allocation (LDA) models, and the like. The prototype information injects prior knowledge to such models, thereby rendering them more accurate, effective, and efficient. For instance, in the context of automated word labeling, additional knowledge is encoded into the models by providing a small set of prototypical words for each possible label. The net result is that words in a given corpus are labeled and are therefore in condition to be summarized, identified, classified, clustered, and the like.
    Type: Grant
    Filed: September 13, 2007
    Date of Patent: August 30, 2011
    Assignee: Microsoft Corporation
    Inventors: Kannan Achan, Moises Goldszmidt, Lev Ratinov
  • Publication number: 20110209001
    Abstract: Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model.
    Type: Application
    Filed: May 4, 2011
    Publication date: August 25, 2011
    Applicant: MICROSOFT CORPORATION
    Inventors: Aleksandr Simma, Moises Goldszmidt
  • Patent number: 7962797
    Abstract: The present invention extends to methods, systems, and computer program products for automatically generating and refining health models. Embodiments of the invention use machine learning tools to analyze historical telemetry data from a server deployment. The tools output fingerprints, for example, small groupings of specific metrics-plus-behavioral parameters, that uniquely identify and describe past problem events mined from the historical data. Embodiments automatically translate the fingerprints into health models that can be directly applied to monitoring the running system. Fully-automated feedback loops for identifying past problems and giving advance notice as those problems emerge in the future is facilitated without any operator intervention. In some embodiments, a single portion of expert knowledge, for example, Key Performance Indicator (KPI) data, initiates health model generation.
    Type: Grant
    Filed: March 20, 2009
    Date of Patent: June 14, 2011
    Assignee: Microsoft Corporation
    Inventors: Moises Goldszmidt, Peter Bodik, Hans Christian Andersen
  • Patent number: 7958069
    Abstract: Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model.
    Type: Grant
    Filed: January 16, 2011
    Date of Patent: June 7, 2011
    Assignee: Microsoft Corporation
    Inventors: Aleksandr Simma, Moises Goldszmidt
  • Patent number: 7949745
    Abstract: An activity model is generated at a computer. The activity model may be generated by monitoring incoming and outgoing data in the computer. The collected data is analyzed to form a graph that describes and predicts what output is generated in response to received input. Later, a window of input and output data is collected from the computer. This collected window of data is used to query the activity model. The graph in the activity model is then used to give the probability that the collected window of data was collected from the computer used to generate the activity model. A high probability indicates that the computer is performing normally, while a low probability indicates that the computer may behaving erratically and there may be a problem with the computer.
    Type: Grant
    Filed: October 31, 2006
    Date of Patent: May 24, 2011
    Assignee: Microsoft Corporation
    Inventors: Paul Barham, Richard Black, Moises Goldszmidt, Rebecca Isaacs, John MacCormick, Richard Mortier
  • Publication number: 20110113004
    Abstract: Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model.
    Type: Application
    Filed: January 16, 2011
    Publication date: May 12, 2011
    Applicant: MICROSOFT CORPORATION
    Inventors: Aleksandr Simma, Moises Goldszmidt
  • Patent number: 7895146
    Abstract: Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model.
    Type: Grant
    Filed: December 3, 2007
    Date of Patent: February 22, 2011
    Assignee: Microsoft Corporation
    Inventors: Aleksandr Simma, Moises Goldszmidt
  • Publication number: 20100306597
    Abstract: Methods for automatically identifying and classifying a crisis state occurring in a system having a plurality of computer resources. Signals are received from a device that collects the signals from each computer resource in the system. For each epoch, an epoch fingerprint is generated. Upon detecting a performance crisis within the system, a crisis fingerprint is generated consisting of at least one epoch fingerprint. The technology is able to identify that a performance crisis has previously occurred within the datacenter if a generated crisis fingerprint favorably matches any of the model crisis fingerprints stored in a database. The technology may also predict that a crisis is about to occur.
    Type: Application
    Filed: May 28, 2009
    Publication date: December 2, 2010
    Applicant: MICROSOFT CORPORATION
    Inventors: Moises Goldszmidt, Peter Bodik
  • Patent number: 7821947
    Abstract: An activity model is generated at a computer. The activity model may be generated by monitoring incoming and outgoing channels for packets for a predetermined window of time. To generate an activity model, an input and an output channel are selected. A probability distribution function describing the observed waiting time between packet arrivals on the selected input channel and the selected output channel is generated by mining the data collected during the selected window of time. A probability distribution function describing the observed waiting time between a randomly chosen instant and receiving a packet on the selected input channel is also generated. The distance between the two generated probability distribution functions is computed. If the computed distance is greater than a predefined confidence level, then the two selected channels are deemed to be related. Otherwise, the selected channels are deemed to be unrelated.
    Type: Grant
    Filed: April 24, 2007
    Date of Patent: October 26, 2010
    Assignee: Microsoft Corporation
    Inventors: John MacCormick, Paul Barham, Moises Goldszmidt
  • Publication number: 20100241903
    Abstract: The present invention extends to methods, systems, and computer program products for automatically generating and refining health models. Embodiments of the invention use machine learning tools to analyze historical telemetry data from a server deployment. The tools output fingerprints, for example, small groupings of specific metrics-plus-behavioral parameters, that uniquely identify and describe past problem events mined from the historical data. Embodiments automatically translate the fingerprints into health models that can be directly applied to monitoring the running system. Fully-automated feedback loops for identifying past problems and giving advance notice as those problems emerge in the future is facilitated without any operator intervention. In some embodiments, a single portion of expert knowledge, for example, Key Performance Indicator (KPI) data, initiates health model generation.
    Type: Application
    Filed: March 20, 2009
    Publication date: September 23, 2010
    Applicant: Microsoft Corporation
    Inventors: Moises Goldszmidt, Peter Bodik, Hans Christian Andersen
  • Patent number: 7721061
    Abstract: An embodiment of a method of predicting response time for a storage request begins with a first step of a computing entity storing a training data set. The training data set comprises past performance observations for past storage requests of a storage array. Each past performance observation comprises an observed response time and a feature vector for a particular past storage request. The feature vector includes characteristics that are available external to the storage array. In a second step, the computing entity forms a response time forecaster from the training data set. In the third step, the computing entity applies the response time forecaster to a pending feature vector for a pending storage request to obtain a predicted response time for the pending storage request.
    Type: Grant
    Filed: June 22, 2005
    Date of Patent: May 18, 2010
    Assignee: Hewlett-Packard Development Company, L.P.
    Inventors: Terence P. Kelly, Ira Cohen, Moises Goldszmidt, Kimberly K. Keeton
  • Publication number: 20100115216
    Abstract: In a distributed storage system such as those in a data center or web based service, user characteristics and characteristics of the hardware such as storage size and storage throughput impact the capacity and performance of the system. In such systems, an allocation is a mapping from the user to the physical storage devices where data/information pertaining to the user will be stored. Policies regarding quality of service and reliability including replication of user data/information may be provided by the entity managing the system. A policy may define an objective function which quantifies the value of a given allocation. Maximizing the value of the allocation will optimize the objective function. This optimization may include the dynamics in terms of changes in patterns of user characteristics and the cost of moving data/information between the physical devices to satisfy a particular allocation.
    Type: Application
    Filed: November 4, 2008
    Publication date: May 6, 2010
    Applicant: MICROSOFT CORPORATION
    Inventors: Hongzhong Jia, Moises Goldszmidt
  • Patent number: 7693982
    Abstract: Systems, methods, and software used in performing automated diagnosis and identification of or forecasting service level object states. Some embodiments include building classifier models based on collected metric data to detect and forecast service level objective (SLO) violations. Some such systems, methods, and software further include automated detecting and forecasting of SLO violations along with providing alarms, messages, or commands to administrators or system components. Some such messages include diagnostic information with regard to a cause of a SLO violation. Some embodiments further include storing data representative of system performance and detected and forecast system SLO states. This data can then be used to generate reports of system performance including representations of system SLO states.
    Type: Grant
    Filed: November 12, 2004
    Date of Patent: April 6, 2010
    Assignee: Hewlett-Packard Development Company, L.P.
    Inventors: Moises Goldszmidt, Ira Cohen, Terence Patrick Kelly, Julie Anna Symons
  • Publication number: 20090144034
    Abstract: Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model.
    Type: Application
    Filed: December 3, 2007
    Publication date: June 4, 2009
    Applicant: MICROSOFT CORPORATION
    Inventors: Aleksandr Simma, Moises Goldszmidt
  • Publication number: 20090076794
    Abstract: Mechanisms are disclosed for incorporating prototype information into probabilistic models for automated information processing, mining, and knowledge discovery. Examples of these models include Hidden Markov Models (HMMs), Latent Dirichlet Allocation (LDA) models, and the like. The prototype information injects prior knowledge to such models, thereby rendering them more accurate, effective, and efficient. For instance, in the context of automated word labeling, additional knowledge is encoded into the models by providing a small set of prototypical words for each possible label. The net result is that words in a given corpus are labeled and are therefore in condition to be summarized, identified, classified, clustered, and the like.
    Type: Application
    Filed: September 13, 2007
    Publication date: March 19, 2009
    Applicant: Microsoft Corporation
    Inventors: Kannan Achan, Moises Goldszmidt, Lev Ratinov
  • Patent number: 7502971
    Abstract: A computer system includes a signature creation engine operable to determine signatures representing states of a computer resource from metrics for the computer resource. The computer system also includes a database operable to store the signatures along with an annotation for each signature including information relating to a state of the computer resource. The computer system is operable to determine a recurrent problem of the computer resource from stored signatures.
    Type: Grant
    Filed: October 12, 2005
    Date of Patent: March 10, 2009
    Assignee: Hewlett-Packard Development Company, L.P.
    Inventors: Ira Cohen, Moises Goldszmidt, Julie A. Symons, Terence P. Kelly, Steve Y. Zhang
  • Patent number: 7451226
    Abstract: A method of grouping content requests by one or more behaviors is provided. Each content request is labeled. Sessions for various user and service types are defined. The sessions are then modeled to create representative sessions. Each session is then matched with one or more representative sessions.
    Type: Grant
    Filed: September 25, 2006
    Date of Patent: November 11, 2008
    Assignee: Entrust, Inc.
    Inventors: Moises Goldszmidt, Bikash Sabata, Derek Palma, Amitava Raha
  • Publication number: 20080267083
    Abstract: An activity model is generated at a computer. The activity model may be generated by monitoring incoming and outgoing channels for packets for a predetermined window of time. To generate an activity model, an input and an output channel are selected. A probability distribution function describing the observed waiting time between packet arrivals on the selected input channel and the selected output channel is generated by mining the data collected during the selected window of time. A probability distribution function describing the observed waiting time between a randomly chosen instant and receiving a packet on the selected input channel is also generated. The distance between the two generated probability distribution functions is computed. If the computed distance is greater than a predefined confidence level, then the two selected channels are deemed to be related. Otherwise, the selected channels are deemed to be unrelated.
    Type: Application
    Filed: April 24, 2007
    Publication date: October 30, 2008
    Applicant: Microsoft Corporation
    Inventors: John MacCormick, Paul Barham, Moises Goldszmidt
  • Patent number: 7441028
    Abstract: A method is provided for defining a required information delivery system capacity as a function of a user's service quality objectives. An information delivery system behavior is modeled to understand under what conditions the user's service quality objectives are met or not met. Conditions are captured in which the user's service quality objectives are met or not met. Statistical techniques are applied to the conditions captured. A model is induced that describes the conditions in which the user's service quality objectives are met or not met.
    Type: Grant
    Filed: September 25, 2006
    Date of Patent: October 21, 2008
    Assignee: Entrust, Inc.
    Inventors: Moises Goldszmidt, Bikash Sabata, Derek Palma, Amitava Raha