Emc IP Holding Company LLC

Dell Products Inc.

Dell & Products L.p.

Emc Corporation

Landmark Graphics Corporation

Vinicius Michel Gottin

Paulo Abelha Ferreira

Pablo Nascimento Da Silva

Rômulo Teixeira De Abreu Pinho

Tiago Salviano Calmon

Jonas Furtado Dias

Joel Evans Christner

Hugo De Oliveira Barbalho

Alex Laier Bordignon

Roberto Nery Stelling Neto

Romulo D Pinho

Eduardo Vera Sousa

Daniel Sadoc Menasché

Angelo E M Ciarlini

Paulo De Figueiredo Pires

Herberth Birck Fröhlich

Romulo Teixeira De Abreu Pinho

Keshava Prasad Rangarajan

Scott David Senften

Nalinkumar Mistry

Ashwani Dev

Joseph Blake Winston

Werner Spolidoro Freund

Jaumir Valença Da Silveira Junior

Julia Drummond Noce

David Michael Holmes

Xi Wang

Edward Pacheco

Raul Gracia

Chandra Yeleshwarapu

Eric L Caron

Angelo Ciarlini

Jaumir Valenca Da Silveira Junior

Clifton Brent Walker

Andre Maximo

Adriana Bechara Prado

André De Almeida Maximo

Edward José Pacheco Condori

Fábio André Machado Porto

Wagner Dos Santos Vieira

Yania Molina Souto

Nagaraj Srinivasan

Bruno Carlos Da Cunha Costa

Percy Rivera Salas

Renan De Campos

Nagaraj Sirinivasan

Franklin Jordan Ventura Quico

Norton Zamboni Junior

Fagner Scheibel Reinaldo

Aline Rodrigues Da Cruz

Paulo Acassio Martins Oliveira

Franklin Ventura

Occupancy aware decision-making is disclosed. Positions of entities in an environment are determined. Stationary entities are used as markers to segment the environment. Positions of non-stationary entities are used to determine an occupancy score for each of the segments. Decision making operations, such as pathfinding for mobile entities, can be performed using the occupancy scores of the segments. The occupancy scores and segmentation adapt to changes in the environment.

Dynamic segmentation and occupancy scoring in edge environments with mobile devices for real-time decision making

An event driven detection model is disclosed. A model operates at a node to identify relevant video data from video streams generated by cameras. Video data that is not relevant is discarded. An objectness score is generated for the relevant video data. The objectness score and position data from position sensors is used to infer an event. When an event is inferred by the model, a decision may be made and performed.

Efficient event-driven object detection at the forklifts at the edge in warehouse environments

One example method includes selecting items to be repatriated from a cloud site to an on-premises site, and the items include a workload and a data set accessed by the workload, transmitting a repatriation request from the on-premises site to the cloud site, and the repatriation request identifies the selected items, receiving, by the on-premises site from the cloud site, a compressed data set which includes the data set in compressed form, receiving, by the on-premises site from the cloud site, a compressed workload which includes the workload in compressed form, and the compressed workload and the compressed data set have been compressed with a compression algorithm automatically selected based on content, and/or context, of data in the data set, decompressing, at the on-premises site, the compressed data set and the compressed workload, and deploying the decompressed data set and the decompressed workload locally at the on-premises site.

Opportunistic on-prem data repatriation in hybrid clouds

One example method includes mapping of a set of environment constraints to various elements of a dataset distillation process, and then performing the dataset distillation process based upon the mapping, and the dataset distillation process is performed in a distributed manner by a group of edge nodes and a central node with which the edge nodes communicate. The environment constraints may include computing resource availability, and privacy constraints concerning the data of the dataset.

Adaptable dataset distillation for heterogeneous environments

The quality of decompressed data that was compressed in a lossy manner to generate lossy compressed data, is improved. Lossy compressed data may be generated at a device and transmitted to a gateway. The gateway includes a reconstruction engine that is trained to reduce the compression loss by generating a learned residue. The residue is added to the lossy decompressed data to generated corrected decompressed data that has a smaller loss than the lossy decompressed data.

Quality of lossy compressed sensor data with machine learning

One example method includes performing operations, in a computing environment that includes multiple layers, one of which is a functional edge layer, that include obtaining input parameters, and one of the input parameters identifies one of the layers of the computing environment where a machine learning model is located, based on the input parameters, placing a control module in one of the layers of the computing environment, determining, by the control module, candidate layers of the computing environment for respective instances of a drift module, and placing a respective one of the instances of the drift module in each of the candidate layers, running the control module to identify an instance of the drift module to be activated, and activating the identified instance of the drift module, and detecting, by the activated instance of the drift module, drift in the machine learning model.

Self-distributed multimodal drift detection at the edge

One example method includes identifying features for operational actions that are to be performed at an edge node of an edge computing system. The features include information used by the edge node to execute the operational actions. A first set of the features is received during an input processing stage. The first set of the features are used in the execution of the operational actions. In response to the execution of the operational actions, a second set of the one or more features are obtained, In response to retrieving the first set of the features or in response to obtaining the second set of the features, a local map is updated to include the features. The local map includes a subset of a global map, where the local map and the global map represent a shared representation of a map structure.

Orchestration of action-input representations for decision making in edge environments

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.

Fast converging gradient compressor for federated learning

Peer-based positioning of nodes in an environment is disclosed. A node is able to determine its own position using sources. The sources may include fixed and/or mobile beacons. Each of the sources is associated with a distance error and a position error. These errors, along with distance measurements between the node and each of the sources, are used to determine a position and a total positioning error, which reflects the accuracy of the determined position.

Peer-based positioning of mobile devices

Systems and methods for decoupling information into multiple levels of detail based on semantics while providing a compact global representation structure. A tree-based graph represents objects in an environment. The top node provides complete environment information at the lowest level of detail. Leaves of the tree-based graph complement the parent nodes with more detailed local information. This achieved efficient processing, communication, and data compaction.

So-map: a semantic-aware algorithm for optimizing the representation structure of Octomaps

Techniques are provided for customer service ticket similarity determination using an updated encoding model based on similarity feedback from a user. One method comprises obtaining encodings of customer service tickets in a vector space using an encoding model; determining pairwise similarities for the encodings of the customer service tickets; obtaining feedback from a user regarding the pairwise similarities for a subset of the encodings; updating pairwise similarities for the subset of the encodings using the feedback from the user; generating an updated encoding model by processing the updated pairwise similarities for the subset of the encodings of the customer service tickets using a supervised learning algorithm; and processing at least one customer service ticket based at least in part on the updated encoding model. The feedback from the user may indicate a similarity of two or more of the plurality of customer service tickets.

Customer service ticket similarity determination using updated encoding model based on similarity feedback from user

Techniques for edge-enabled trajectory map generation are disclosed herein. For example, a method can include segmenting one or more node trajectories based on data collected at a node, determining one or more initial trajectories based on the segmented node trajectories, and generating a map including trajectories generated by associating attribute data and event data with the initial trajectories.

Edge-enabled trajectory map generation

Real-time event detection is performed on nodes in an environment using position data that is not available to a node in real time but is delayed. A node performs real time event detection by predicting a position of the node based at least in part on delayed position data. The delayed position data is aligned to other sensor data. Aligning the position data may include predicting a position based on dead reckoning and/or a machine learning model. One or more collections of data, each collection including sensor data and predicted position data, is input to a model that performs event detection.

Event detection on far edge mobile devices using delayed positioning data

Techniques are disclosed for predicting a table column using machine learning. For example, a system can include at least one processing device including a processor coupled to a memory, the processing device being configured to implement the following: determining a local word density for words in a table, the local word density measuring a count of other words in a first region surrounding the words; determining a local numeric density for the words, the local numeric density measuring a proportion of digits in a second region surrounding the words; determining semantic associations for the words by processing the words using an ML-based semantic association model trained based on surrounding words in nearby table columns and rows; and predicting a table column index for the words by processing the table using an ML-based table column model trained based on the local word density, local numeric density, and semantic association.

Table column identification using machine learning

One example method includes determining a system aggregate drift metric score based on aggregate drift metric scores received from near-edge nodes associated with a central node. The system aggregate drift metric score indicates a level of model drift across a logistics system. The system aggregate drift metric score is compared with a system drift threshold. Current datasets are received from the near-edge nodes when a system aggregate drift metric score is greater than the system drift threshold. A first dataset is generated comprising a joining of the current datasets received from the plurality of near-edge nodes. A second dataset is received from each the near-edge nodes. The first and second datasets are used to select a machine-learning (ML) model to deploy at each of the near-edge nodes.

Bootstrap method for continuous deployment in cross-customer model management

Growing community of inventors

Rio de Janeiro, Brazil

Vinicius Michel Gottin