Patents by Inventor Roberto Nery Stelling Neto
Roberto Nery Stelling Neto 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: 20240111868Abstract: One example method includes dynamically monitoring a stream of image portions that have been classified by a segmentation model of a video surveillance system, evaluating the image portions, based on the evaluating, determining that an attack on the video surveillance system is occurring, or has occurred, and implementing, or causing the implementation of, a remedial action with regard to the attack. The image portions may be image portions that have been classified by a segmentation model.Type: ApplicationFiled: October 4, 2022Publication date: April 4, 2024Inventors: Pablo Nascimento da Silva, Hugo de Oliveira Barbalho, Roberto Nery Stelling Neto
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Publication number: 20230334319Abstract: One example method includes, in an edge node, of a group of edge nodes that are each operable to communicate with a central node, performing operations that include generating a vector that includes gradients associated with a model instance, of a central model, that is operable to run at the edge node, performing a check to determine whether the model instance is overfitting to data generated at the edge node, and either performing sign compression on the vector when overfitting is not indicated, or performing random perc sign compression on the vector when overfitting is indicated, and transmitting the vector, after compression, to the central node that includes the central model.Type: ApplicationFiled: April 13, 2022Publication date: October 19, 2023Inventors: Paulo Abelha Ferreira, Pablo Nascimento Da Silva, Roberto Nery Stelling Neto, Vinicius Michel Gottin
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Patent number: 11790039Abstract: Methods for compression switching that includes distributing a model to client nodes, which use the model to generate a gradient vector (GV) based on a client node data set. The method includes receiving a model update that includes a gradient sign vector (GSV) based on the gradient vector; generating an updated model using the GSV; and distributing the updated model to the client nodes. The client node uses the updated model to generate a second GV based on a second client node data set. The method also includes a determination that a compression switch condition exists; based on the determination, transmitting an instruction to the client node to perform a compression switch; receiving, in response to the instruction, another model update including a subset GSV based on the second gradient vector; generating a second updated model using the subset GSV; and distributing the second updated model to the client nodes.Type: GrantFiled: October 29, 2020Date of Patent: October 17, 2023Assignee: EMC IP Holding Company LLCInventors: Paulo Abelha Ferreira, Pablo Nascimento Da Silva, Tiago Salviano Calmon, Roberto Nery Stelling Neto, Vinicius Michel Gottin
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Publication number: 20230315607Abstract: One example method includes accessing input data elements from logs that identify user problems with computing system components, the data elements each associated with a respective original class label that identifies a class of computing system components to which the data element relates, the respective original class labels forming a group of class labels, and a first of the original class labels is overrepresented in the group, and reducing overrepresentation of the first original class label in the group by creating an arbitrary aggregation of some of the class labels that includes the first original class label. The method includes creating, based on a hierarchical modeling structure, prepared data in which an original class label is replaced by the aggregation. Next a hierarchical model and benchmark model are trained, and each model generates respective predictions for comparison. An inferencing process is performed to determine which predicted label will be used.Type: ApplicationFiled: March 15, 2022Publication date: October 5, 2023Inventors: Rômulo Teixeira de Abreu Pinho, Adriana Bechara Prado, Roberto Nery Stelling Neto, Jeffrey Scott Vah, Aaron Sanchez, Ravi Shukla
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Patent number: 11762752Abstract: Facilitating detection of anomalies of a target entity is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise training a model on a first set of variables that are constrained by a second set of variables. The second set of variables can characterize elements of a defined entity. The first set of variables can define a normality of the defined entity. The operations also can comprise employing the model to identify expected parameters and unexpected parameters associated with the defined entity to at least a defined level of confidence.Type: GrantFiled: April 20, 2022Date of Patent: September 19, 2023Assignee: EMC IP HOLDING COMPANY LLCInventors: Rômulo Teixeira de Abreu Pinho, Vitor Silva Sousa, Rodrigo Rios Almeida de Souza, Roberto Nery Stelling Neto
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Publication number: 20230132330Abstract: One example method includes deploying a discriminator, where the discriminator is trained to recognize an adversarial image received by the discriminator as adversarial, and the adversarial image is generated based upon an original image, the adversarial image including a perturbation that cannot be detected by a human eye but which is effective to deceive an image segmentation model to misclassify the original image, receiving, by the discriminator, an image captured by an autonomous vehicle, and determining, by the discriminator, whether the image received from the autonomous vehicle is adversarial.Type: ApplicationFiled: October 21, 2021Publication date: April 27, 2023Inventors: Hugo de Oliveira Barbalho, Pablo Nascimento da Silva, Roberto Nery Stelling Neto
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Patent number: 11416506Abstract: Facilitating temporal data management for anomalous state detection in data centers is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise performing a process of extraction, transformation, and loading of data from log files into a telemetry data store. The data can be loaded into the telemetry data store as telemetry data. The operations also can comprise dividing the telemetry data into first telemetry data and second telemetry data. The first telemetry data can comprise telemetry data that does not satisfy a defined quality level. The second telemetry data can comprise telemetry data that satisfies the defined quality level. Further, the operations can comprise removing the first telemetry data from the telemetry data store and outputting the second telemetry data based on a request for the second telemetry data.Type: GrantFiled: April 29, 2020Date of Patent: August 16, 2022Assignee: EMC IP Holding Company LLCInventors: Rômulo Teixeira de Abreu Pinho, Vítor Silva Sousa, Rodrigo Rios Almeida de Souza, Roberto Nery Stelling Neto
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Publication number: 20220253370Abstract: Facilitating detection of anomalies of a target entity is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise training a model on a first set of variables that are constrained by a second set of variables. The second set of variables can characterize elements of a defined entity. The first set of variables can define a normality of the defined entity. The operations also can comprise employing the model to identify expected parameters and unexpected parameters associated with the defined entity to at least a defined level of confidence.Type: ApplicationFiled: April 20, 2022Publication date: August 11, 2022Inventors: Rômulo Teixeira de Abreu Pinho, Vítor Silva Sousa, Rodrigo Rios Almeida de Souza, Roberto Nery Stelling Neto
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Publication number: 20220230092Abstract: Method for model updating in a federated learning environment, including distributing a current model to client nodes; receiving a first set of gradient sign vectors, wherein each gradient sign vector of the first set of gradient sign vectors is received from one client node; generating a first updated model based on the first set of gradient sign vectors; distributing the first updated model to the plurality of client nodes; storing a first shape parameter and a second shape parameter; receiving, in response to distributing the first updated model, a second set of gradient sign vectors, wherein each gradient sign vector of the second set of gradient sign vectors is received from one client node; generating a second updated model based on the second set of gradient sign vectors, the first shape parameter, and the second shape parameter; and distributing the second updated model to the plurality of client nodes.Type: ApplicationFiled: January 21, 2021Publication date: July 21, 2022Inventors: Paulo Abelha Ferreira, Pablo Nascimento da Silva, Tiago Salviano Calmon, Roberto Nery Stelling Neto, Vinicius Michel Gottin
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Patent number: 11379375Abstract: An information handling system for managing a storage system includes storage for storing profile-based cache policy performance prediction models. The information handling system also includes a storage manager that obtains an input-output profile for a workload hosted by the information handling system during a first period of time; obtains performance metrics for cache policies for the storage system using: the input-output profile, and the profile-based cache policy performance prediction models; obtains a ranking of the cache policies based on the performance metrics; selects a cache policy of the cache policies based on the rankings; and updates operation of a cache of the storage system based on the selected cache policy for a second period of time.Type: GrantFiled: April 20, 2021Date of Patent: July 5, 2022Assignee: EMC IP Holding Company LLCInventors: Vinicius Michel Gottin, Hugo de Oliveira Barbalho, Rômulo Teixeira de Abreu Pinho, Roberto Nery Stelling Neto, Alex Laier Bordignon, Daniel Sadoc Menasché
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Patent number: 11341026Abstract: Facilitating detection of anomalies of a target entity is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise training a model on a first set of variables that are constrained by a second set of variables. The second set of variables can characterize elements of a defined entity. The first set of variables can define a normality of the defined entity. The operations also can comprise employing the model to identify expected parameters and unexpected parameters associated with the defined entity to at least a defined level of confidence.Type: GrantFiled: January 6, 2020Date of Patent: May 24, 2022Assignee: EMC IP Holding Company LLCInventors: Rômulo Teixeira de Abreu Pinho, Vitor Silva Sousa, Rodrigo Rios Almeida de Souza, Roberto Nery Stelling Neto
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Publication number: 20220138498Abstract: Methods for compression switching that includes distributing a model to client nodes, which use the model to generate a gradient vector (GV) based on a client node data set. The method includes receiving a model update that includes a gradient sign vector (GSV) based on the gradient vector; generating an updated model using the GSV; and distributing the updated model to the client nodes. The client node uses the updated model to generate a second GV based on a second client node data set. The method also includes a determination that a compression switch condition exists; based on the determination, transmitting an instruction to the client node to perform a compression switch; receiving, in response to the instruction, another model update including a subset GSV based on the second gradient vector; generating a second updated model using the subset GSV; and distributing the second updated model to the client nodes.Type: ApplicationFiled: October 29, 2020Publication date: May 5, 2022Inventors: Paulo Abelha Ferreira, Pablo Nascimento Da Silva, Tiago Salviano Calmon, Roberto Nery Stelling Neto, Vinicius Michel Gottin
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Publication number: 20220137852Abstract: A method for managing storage devices includes obtaining, by a storage device event manager, a set of storage device telemetry snapshots is associated with a set of storage devices, generating a telemetry summary correlation matrix using the set of storage device telemetry snapshots, performing, using the telemetry summary correlation matrix, a classification of each storage device in the set of storage devices to obtain a set of classification tags using a first portion of a set of features, obtaining a set of normality states for the set of storage devices using the set of classification tags and a second portion of the set of features, updating an event anomaly policy based on the set of normality states, and performing a remediation action on a storage device in the set of storage devices based on the event anomaly policy.Type: ApplicationFiled: October 29, 2020Publication date: May 5, 2022Inventors: Rômulo Teixeira De Abreu Pinho, Roberto Nery Stelling Neto, Rodrigo Rios Almeida De Souza, Vitor Silva Sousa
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Publication number: 20220129786Abstract: A framework for rapidly prototyping federated learning algorithms. Specifically, the disclosed framework proposes a method and system for evaluating different hypotheses for configuring learning model, which may be optimized through federated learning. Through the disclosed framework, these hypotheses may be tested for scalability, hardware and network resource performance, as well as for new learning state compression and/or aggregation technique effectiveness. Further, these hypotheses may be tested through federated learning simulations, which avoid costs associated with deploying these hypotheses to be tested across production systems.Type: ApplicationFiled: October 27, 2020Publication date: April 28, 2022Inventors: Pablo Nascimento da Silva, Paulo Abelha Ferreira, Tiago Salviano Calmon, Roberto Nery Stelling Neto, Vinicius Michel Gottin
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Publication number: 20220101178Abstract: An adaptive distributed learning model optimization for performance prediction under data privacy constraints. Specifically, the disclosed method and system introduce a framework through which a shared machine learning model deployed across a network of computing nodes may be optimized using private and decentralized datasets. Through the proposed framework, the shared machine learning model may achieve a good generalization error globally across the network, and may also achieving good predictive performance locally while employed on each computing node.Type: ApplicationFiled: September 25, 2020Publication date: March 31, 2022Inventors: Pablo Nascimento Da Silva, Paulo Abeiha Ferreira, Tiago Salviano Calmon, Vinicius Michel Gottin, Roberto Nery Stelling Neto
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Publication number: 20210383197Abstract: A method for adaptive stochastic learning state compression for federated learning in infrastructure domains. Specifically, the disclosed method introduces an adaptive data compressor directed to reducing the amount of information exchanged between nodes participating in the optimization of a shared machine learning model through federated learning. The adaptive data compressor may employ stochastic k-level quantization, and may include functionality to handle exceptions stemming from the detection of unbalanced and/or irregularly sized data.Type: ApplicationFiled: June 4, 2020Publication date: December 9, 2021Inventors: Pablo Nascimento Da Silva, Paulo Abelha Ferreira, Roberto Nery Stelling Neto, Tiago Salviano Calmon, Vinicius Michel Gottin
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Patent number: 11194725Abstract: A cache management system includes a sequentiality determination process configured to determine sequentiality profiles of a workload of IO traces as the workload dynamically changes over time. A learning process is trained to learn a correlation between workload sequentiality and cache pollution, and the trained learning process is used to predict cache pollution before the cache starts to experience symptoms of excessive pollution. The predicted pollution value is used by a cache policy adjustment process to change the prefetch policy applied to the cache, to proactively control the manner in which prefetching is used to write data to the cache. Selection of the cache policy is implemented on a per-LUN basis, so that cache performance for each LUN is individually managed by the cache management system.Type: GrantFiled: November 15, 2019Date of Patent: December 7, 2021Assignee: Dell Products, L.P.Inventors: Rômulo Teixeira de Abreu Pinho, Hugo de Oliveira Barbalho, Vinicius Michel Gottin, Roberto Nery Stelling Neto, Alex Laier Bordignon, Daniel Sadoc Menasché
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Patent number: 11182321Abstract: Techniques are provided for characterizing and quantifying a sequentiality of workloads using sequentiality profiles and signatures. One exemplary method comprises obtaining telemetry data for an input/output workload; evaluating a distribution over time of sequence lengths for input/output requests in the telemetry data by the input/output workload; and generating a sequentiality profile for the input/output workload to characterize the input/output workload based at least in part on the distribution over time of the sequence lengths. Multiple sequentiality profiles for one or more input/output workloads may be clustered into a plurality of clusters. A sequentiality signature may be generated to represent one or more sequentiality profiles within a given cluster. A performance of data movement policies may be evaluated with respect to the sequentiality signature of the given cluster.Type: GrantFiled: November 1, 2019Date of Patent: November 23, 2021Assignee: EMC IP Holding Company LLCInventors: Rômulo Teixeira de Abreu Pinho, Hugo de Oliveira Barbalho, Vinícius Michel Gottin, Roberto Nery Stelling Neto, Alex Laier Bordignon, Daniel Sadoc Menasché
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Publication number: 20210342347Abstract: Facilitating temporal data management for anomalous state detection in data centers is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise performing a process of extraction, transformation, and loading of data from log files into a telemetry data store. The data can be loaded into the telemetry data store as telemetry data. The operations also can comprise dividing the telemetry data into first telemetry data and second telemetry data. The first telemetry data can comprise telemetry data that does not satisfy a defined quality level. The second telemetry data can comprise telemetry data that satisfies the defined quality level. Further, the operations can comprise removing the first telemetry data from the telemetry data store and outputting the second telemetry data based on a request for the second telemetry data.Type: ApplicationFiled: April 29, 2020Publication date: November 4, 2021Inventors: Rômulo Teixeira de Abreu Pinho, Vítor Silva Sousa, Rodrigo Rios Almeida de Souza, Roberto Nery Stelling Neto
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Publication number: 20210208995Abstract: Facilitating detection of anomalies of a target entity is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise training a model on a first set of variables that are constrained by a second set of variables. The second set of variables can characterize elements of a defined entity. The first set of variables can define a normality of the defined entity. The operations also can comprise employing the model to identify expected parameters and unexpected parameters associated with the defined entity to at least a defined level of confidence.Type: ApplicationFiled: January 6, 2020Publication date: July 8, 2021Inventors: Rômulo Teixeira de Abreu Pinho, Vítor Silva Sousa, Rodrigo Rios Almeida de Souza, Roberto Nery Stelling Neto