Patents by Inventor Elmira M. Bonab
Elmira M. Bonab 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).
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Publication number: 20240118966Abstract: A data processing system comprising a processor having a working memory and processing logic, a boot system configured to load one or more algorithms for initializing a basic input output system (BIOS) of the processor into the working memory and an error correction system configured to start a watchdog timer and to monitor initialization of the processor, the error correction system further configured to implement a corrective process if the watchdog timer times out prior to initialization of the BIOS of the processor.Type: ApplicationFiled: October 5, 2022Publication date: April 11, 2024Applicant: DELL PRODUCTS L.P.Inventors: Ibrahim Sayyed, Chris Edward Pepper, Christopher Channing Griffin, Elmira M. Bonab, Purushothama R. Malluru
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Publication number: 20240103837Abstract: Systems and methods for providing a seamless and secure motherboard replacement system and method are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions that, upon execution, cause the IHS to when a previous motherboard is replaced with a replacement motherboard; detect that the previous motherboard has been replaced with the replacement motherboard, access context data associated with the previous motherboard from a storage unit configured in the IHS, the context data comprising configuration settings of the previous motherboard, and update the replacement motherboard according to the stored context information.Type: ApplicationFiled: September 27, 2022Publication date: March 28, 2024Applicant: Dell Products, L.P.Inventors: Ibrahim Sayyed, Shekar Babu Suryanarayana, Elmira M. Bonab
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Patent number: 11880750Abstract: A method of detecting deviation from an operational state of a rotational device includes receiving, from one or more sensor devices coupled to the rotational device, frequency domain data indicative of vibration data sensed during a sensing period. The method also includes processing the frequency domain data using a trained anomaly detection model to generate an anomaly score for the sensing period and processing the anomaly score using an alert generation model to determine whether to generate an alert.Type: GrantFiled: April 15, 2020Date of Patent: January 23, 2024Assignee: SPARKCOGNITION, INC.Inventors: Alexandru Ardel, Shashank Bassi, Elmira M Bonab, Jeff Brown
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Patent number: 11853893Abstract: A method includes generating, by a processor of a computing device, a first plurality of models (including a first number of models) based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm. The method includes determining whether to modify an epoch size for the genetic algorithm during a second epoch of the genetic algorithm based on a convergence metric associated with at least one epoch that is prior to the second epoch. The second epoch is subsequent to the first epoch. The method further includes, based on determining to modify the epoch size, generating a second plurality of models (including a second number of models that is different than the first number) based on the genetic algorithm and corresponding to the second epoch. Each model of the first plurality of models and the second plurality of models includes data representative of neural networks.Type: GrantFiled: June 1, 2021Date of Patent: December 26, 2023Assignee: SPARKCOGNITION, INC.Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi
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Patent number: 11734604Abstract: A method of detecting deviation from an operational state of a rotational device includes receiving, from one or more sensor devices coupled to the rotational device, frequency domain data indicative of vibration data sensed during a sensing period. The method also includes processing the frequency domain data using a trained anomaly detection model to generate an anomaly score for the sensing period and processing the anomaly score using an alert generation model to determine whether to generate an alert.Type: GrantFiled: April 15, 2020Date of Patent: August 22, 2023Assignee: SPARKCOGNITION, INC.Inventors: Alexandru Ardel, Shashank Bassi, Elmira M Bonab, Jeff Brown
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Patent number: 11610131Abstract: A method includes determining, by a processor of a computing device, a subset of models included in a plurality of models generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes aggregating the subset of models to generate an ensembler. The ensembler, when executed on an input, provides at least a portion of the input to each model of the subset of models to generate a plurality of intermediate outputs. An ensembler output of the ensembler is based on the plurality of intermediate outputs. The method further includes executing the ensembler on input data to determine the ensembler output.Type: GrantFiled: March 6, 2020Date of Patent: March 21, 2023Assignee: SPARKCOGNITION, INC.Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi, Tyler S. McDonnell
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Patent number: 11436069Abstract: A method is provided, comprising: retrieving telemetry data from a first storage device; generating a failure risk score for the first storage device, the failure risk score being generated by using a failure inference engine, the failure inference engine being arranged to execute a predictive failure model for calculating the failure risk score; and outputting the failure risk score for use in servicing the first storage device.Type: GrantFiled: May 1, 2020Date of Patent: September 6, 2022Assignee: Dell Products L.P.Inventors: Patrick Mcguinness, Nagendra-Vikas Kamath, Elmira M. Bonab, WeiTa Chen, Joe Ray
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Publication number: 20220092477Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device, where obtaining the preprocessed data includes applying a transform to the data sensed by the one or more sensor devices to generate a set of features in a frequency domain. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.Type: ApplicationFiled: December 7, 2021Publication date: March 24, 2022Inventors: Alexandre Ardel, Shashank Bassi, Elmira M. Bonab, Jeff Brown
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Patent number: 11227236Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.Type: GrantFiled: April 26, 2021Date of Patent: January 18, 2022Assignee: SPARKCOGNITION, INC.Inventors: Alexandre Ardel, Shashank Bassi, Elmira M Bonab, Jeff Brown
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Publication number: 20210390416Abstract: A method includes generating, by a processor of a computing device, an output set of models corresponding to a first epoch of a genetic algorithm and based on an input set of models of the first epoch. The input set and the output set includes data representative of a neural network. The method includes determining a particular model of the output set based on a fitness function. A first topological parameter of a first model of the input set is modified to generate the particular model of the output set. The method includes modifying a probability that the first topological parameter is to be changed by a genetic operation during a second epoch of the genetic algorithm that is subsequent to the first epoch. The method includes generating a second output set of models corresponding to the second epoch and based on the output set and the modified probability.Type: ApplicationFiled: August 27, 2021Publication date: December 16, 2021Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi, Eric O. Korman
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Publication number: 20210342205Abstract: A method is provided, comprising: retrieving telemetry data from a first storage device; generating a failure risk score for the first storage device, the failure risk score being generated by using a failure inference engine, the failure inference engine being arranged to execute a predictive failure model for calculating the failure risk score; and outputting the failure risk score for use in servicing the first storage device.Type: ApplicationFiled: May 1, 2020Publication date: November 4, 2021Applicant: Dell Products L.P.Inventors: Patrick Mcguinness, Nagendra-Vikas Kamath, Elmira M. Bonab, WeiTa Chen, Joe Ray
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Publication number: 20210326759Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.Type: ApplicationFiled: April 26, 2021Publication date: October 21, 2021Inventors: Alexandre Ardel, Shashank Bassi, Elmira M. Bonab, Jeff Brown
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Publication number: 20210326741Abstract: A method of detecting deviation from an operational state of a rotational device includes receiving, from one or more sensor devices coupled to the rotational device, frequency domain data indicative of vibration data sensed during a sensing period. The method also includes processing the frequency domain data using a trained anomaly detection model to generate an anomaly score for the sensing period and processing the anomaly score using an alert generation model to determine whether to generate an alert.Type: ApplicationFiled: April 15, 2020Publication date: October 21, 2021Inventors: Alexandre Ardel, Shashank Bassi, Elmira M. Bonab, Jeff Brown
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Publication number: 20210287097Abstract: A method includes generating, by a processor of a computing device, a first plurality of models (including a first number of models) based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm. The method includes determining whether to modify an epoch size for the genetic algorithm during a second epoch of the genetic algorithm based on a convergence metric associated with at least one epoch that is prior to the second epoch. The second epoch is subsequent to the first epoch. The method further includes, based on determining to modify the epoch size, generating a second plurality of models (including a second number of models that is different than the first number) based on the genetic algorithm and corresponding to the second epoch. Each model of the first plurality of models and the second plurality of models includes data representative of neural networks.Type: ApplicationFiled: June 1, 2021Publication date: September 16, 2021Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi
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Patent number: 11106978Abstract: A method includes generating, by a processor of a computing device, an output set of models corresponding to a first epoch of a genetic algorithm and based on an input set of models of the first epoch. The input set and the output set includes data representative of a neural network. The method includes determining a particular model of the output set based on a fitness function. A first topological parameter of a first model of the input set is modified to generate the particular model of the output set. The method includes modifying a probability that the first topological parameter is to be changed by a genetic operation during a second epoch of the genetic algorithm that is subsequent to the first epoch. The method includes generating a second output set of models corresponding to the second epoch and based on the output set and the modified probability.Type: GrantFiled: September 8, 2017Date of Patent: August 31, 2021Assignee: SPARKCOGNITION, INC.Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi, Eric O. Korman
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Publication number: 20210256369Abstract: A method includes receiving time series source data that is associated with a source asset and that includes a set of classification labels. The method also includes receiving time series target data that is associated with a target asset and that lacks classification labels. The method further includes determining time series representations from the time series source data and the time series target data. The method also includes, based on the set of classification labels included in the time series source data and at least on raw time series data or the time series representations, generating a classifier operable to classify unlabeled data associated with the target asset. The raw time series data includes the time series source data and the time series target data.Type: ApplicationFiled: February 18, 2020Publication date: August 19, 2021Inventors: Alexandre Ardel, Shashank Bassi, Elmira M Bonab, Jeff Brown, Angad Chandorkar
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Patent number: 11074503Abstract: A method includes generating, by a processor of a computing device, a first plurality of models (including a first number of models) based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm. The method includes determining whether to modify an epoch size for the genetic algorithm during a second epoch of the genetic algorithm based on a convergence metric associated with at least one epoch that is prior to the second epoch. The second epoch is subsequent to the first epoch. The method further includes, based on determining to modify the epoch size, generating a second plurality of models (including a second number of models that is different than the first number) based on the genetic algorithm and corresponding to the second epoch. Each model of the first plurality of models and the second plurality of models includes data representative of neural networks.Type: GrantFiled: September 6, 2017Date of Patent: July 27, 2021Assignee: SPARKCOGNITION, INC.Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi
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Publication number: 20200210847Abstract: A method includes determining, by a processor of a computing device, a subset of models included in a plurality of models generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes aggregating the subset of models to generate an ensembler. The ensembler, when executed on an input, provides at least a portion of the input to each model of the subset of models to generate a plurality of intermediate outputs. An ensembler output of the ensembler is based on the plurality of intermediate outputs. The method further includes executing the ensembler on input data to determine the ensembler output.Type: ApplicationFiled: March 6, 2020Publication date: July 2, 2020Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi, Tyler S. McDonnell
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Patent number: 10635978Abstract: A method includes determining, by a processor of a computing device, a subset of models included in a plurality of models generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes aggregating the subset of models to generate an ensembler. The ensembler, when executed on an input, provides at least a portion of the input to each model of the subset of models to generate a plurality of intermediate outputs. An ensembler output of the ensembler is based on the plurality of intermediate outputs. The method further includes executing the ensembler on input data to determine the ensembler output.Type: GrantFiled: October 26, 2017Date of Patent: April 28, 2020Assignee: SPARKCOGNITION, INC.Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi, Tyler S. McDonnell
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Publication number: 20190130277Abstract: A method includes determining, by a processor of a computing device, a subset of models included in a plurality of models generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes aggregating the subset of models to generate an ensembler. The ensembler, when executed on an input, provides at least a portion of the input to each model of the subset of models to generate a plurality of intermediate outputs. An ensembler output of the ensembler is based on the plurality of intermediate outputs. The method further includes executing the ensembler on input data to determine the ensembler output.Type: ApplicationFiled: October 26, 2017Publication date: May 2, 2019Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi, Tyler S. McDonnell