公开(公告)号：US11409594B2

公开(公告)日：2022-08-09

申请号：US16914300

申请日：2020-06-27

Applicant: Intel Corporation

Inventor： Javier Sebastian Turek ,  Vy Vo ,  Javier Perez-Ramirez ,  Marcos Carranza ,  Mateo Guzman ,  Cesar Martinez-Spessot ,  Dario Oliver

IPC: G06F11/07 ,  G06F40/279 ,  G06N3/02 ,  G06F11/30 ,  G06F40/30 ,  G06F40/56

Abstract: Systems, apparatuses and methods may provide for technology that identifies a sequence of events associated with a computer architecture, categorizes, with a natural language processing system, the sequence of events into a sequence of words, identifying an anomaly based on the sequence of words and triggering an automatic remediation process in response to an identification of the anomaly.

公开(公告)号：US20200327731A1

公开(公告)日：2020-10-15

申请号：US16911517

申请日：2020-06-25

Applicant: Intel Corporation

Inventor： Julio Zamora Esquivel ,  Jose Rodrigo Camacho Perez ,  Hector Cordourier Maruri ,  Paulo Lopez Meyer ,  Jesus Cruz Vargas ,  Marcos Carranza ,  Mateo Guzman ,  Dario Oliver ,  Cesar Martinez-Spessot ,  Javier Turek ,  Javier Felip Leon

IPC: G06T19/00 ,  G06N3/04 ,  G06T19/20 ,  G06F3/01

Abstract: Systems, apparatuses and methods may provide for origination camera technology that extracts data from multimodal sensor signals associated with a scene and generates a five-dimensional (5D) representation of the scene, wherein the 5D representation includes a three-dimensional (3D) visual representation, a one-dimensional (1D) temporal representation, and a 1D branch representation. The technology may also store the 5D representation as a set of abstract descriptors.

公开(公告)号：US20200322528A1

公开(公告)日：2020-10-08

申请号：US16907872

申请日：2020-06-22

Applicant: Intel Corporation

Inventor： Mateo Guzman ,  Javier Turek ,  Marcos Carranza ,  Cesar Martinez-Spessot ,  Dario Oliver ,  Javier Felip Leon Felip Leon ,  Mariano Tepper

IPC: H04N5/232 ,  H04N7/18 ,  G06K9/00 ,  G06T7/70

Abstract: Systems, apparatuses and methods may provide for technology that detects an unidentified individual at a first location along a trajectory in a scene based on a video feed of the scene, wherein the video feed is to be associated with a stationary camera, and selects a non-stationary camera from a plurality of non-stationary cameras based on the trajectory and one or more settings of the selected non-stationary camera. The technology may also automatically instruct the selected non-stationary camera to adjust at least one of the one or more settings, capture a face of the individual at a second location along the trajectory, and identify the unidentified individual based on the captured face of the unidentified individual.

公开(公告)号：US20190317885A1

公开(公告)日：2019-10-17

申请号：US16455380

申请日：2019-06-27

Applicant: Intel Corporation

Inventor： Alexander Heinecke ,  Cesar Martinez-Spessot ,  Dario Oliver ,  Justin Gottschlich ,  Marcos Carranza ,  Mateo Guzman ,  Mats Agerstam

IPC: G06F11/36 ,  G06F21/62

Abstract: Apparatus, systems, methods, and articles of manufacture for automated quality assurance and software improvement are disclosed. An example apparatus includes a data processor to process data corresponding to events occurring with respect to a software application in i) a development and/or a testing environment and ii) a production environment. The example apparatus includes a model tool to: generate a first model of expected software usage based on the data corresponding to events occurring in the development and/or the testing environment; and generate a second model of actual software usage based on the data corresponding to events occurring in the production environment. The example apparatus includes a model comparator to compare the first model to the second model. The example apparatus includes a correction generator to generate an actionable recommendation to adjust the development and/or the testing environment to reduce a difference between the first model and the second model.

公开(公告)号：US20190138295A1

公开(公告)日：2019-05-09

申请号：US16235842

申请日：2018-12-28

Applicant: Intel Corporation

Inventor： Mats Agerstam ,  Sindhu Pandian ,  Shubhangi Rajasekhar ,  Mateo Guzman ,  Yatish Mishra ,  Pranav Sanghadia ,  Troy Willes ,  Cesar Martinez-Spessot ,  Lakshmi Talluru

IPC: G06F8/65 ,  H04L29/08 ,  H04L12/66 ,  H04L12/751 ,  H04L12/733 ,  H04L12/24

Abstract: In embodiments, an apparatus for selectively delivering software updates to nodes in a network includes a receiver to receive a software update and a list of nodes of the network scheduled to receive the software update. In embodiments, the apparatus further includes a device management agent (DMA) to: identify a set of traversals to leaf nodes of the list of nodes necessary to traverse all nodes on the list, and distribute the software updates to the nodes on the list using the set of traversals.

公开(公告)号：US12270886B2

公开(公告)日：2025-04-08

申请号：US18309366

申请日：2023-04-28

Applicant: Intel Corporation

Inventor： Yatish Mishra ,  Mats Agerstam ,  Mateo Guzman ,  Sindhu Pandian ,  Shubhangi Rajasekhar ,  Pranav Sanghadia ,  Troy Willes

IPC: H04W4/50 ,  G01R35/00 ,  G06F16/903 ,  G06F18/214 ,  G06F18/2413 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06Q10/04 ,  G06V10/764 ,  H04L67/125 ,  H04W4/70 ,  H04W52/22 ,  H04L67/12

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

公开(公告)号：US12254361B2

公开(公告)日：2025-03-18

申请号：US18541245

申请日：2023-12-15

Applicant: Intel Corporation

Inventor： Marcos Carranza ,  Cesar Martinez-Spessot ,  Mateo Guzman ,  Francesc Guim Bernat ,  Karthik Kumar ,  Rajesh Poornachandran ,  Kshitij Arun Doshi

IPC: G06F9/54 ,  H04L67/133

Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic Application Programming Interface (API) contract generation and conversion. In an example, a first sidecar of a source microservice intercepts a first call to a first API exposed by a destination microservice. The first call makes use of a first API technology specified by a first contract and is originated by the source microservice. An API technology is selected from multiple API technologies. The selected API technology is determined to be different than the first API technology. Based on the first contract, a second contract is dynamically generated that specifies an intermediate API that makes use of the selected API technology. A second sidecar of the destination microservice is caused to generate the intermediate API and connect the intermediate API to the first API.

公开(公告)号：US20240111615A1

公开(公告)日：2024-04-04

申请号：US18541245

申请日：2023-12-15

Applicant: Intel Corporation

Inventor： Marcos Carranza ,  Cesar Martinez-Spessot ,  Mateo Guzman ,  Francesc Guim Bernat ,  Karthik Kumar ,  Rajesh Poornachandran ,  Kshitij Arun Doshi

IPC: G06F9/54 ,  H04L67/133

CPC classification number: G06F9/547 ,  H04L67/133

Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic API contract generation and conversion. In an example, a first sidecar of a source microservice intercepts a first call to a first API exposed by a destination microservice. The first call makes use of a first API technology specified by a first contract and is originated by the source microservice. An API technology is selected from multiple API technologies. The selected API technology is determined to be different than the first API technology. Based on the first contract, a second contract is dynamically generated that specifies an intermediate API that makes use of the selected API technology. A second sidecar of the destination microservice is caused to generate the intermediate API and connect the intermediate API to the first API.

公开(公告)号：US11880727B2

公开(公告)日：2024-01-23

申请号：US17556682

申请日：2021-12-20

Applicant: Intel Corporation

Inventor： Marcos Carranza ,  Cesar Martinez-Spessot ,  Mateo Guzman ,  Francesc Guim Bernat ,  Karthik Kumar ,  Rajesh Poornachandran ,  Kshitij Arun Doshi

IPC: G06F9/54 ,  H04L67/133

CPC classification number: G06F9/547 ,  H04L67/133

Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic Application Programming Interface (API) contract generation and conversion. In an example, a first call by a first microservice to a first API of a second microservice is intercepted by a first sidecar of the first microservice. The first API is of a first API type of multiple API types and is specified by a first contract. An API type of the multiple API types is selected by the first sidecar. Responsive to determining the selected API type differs from the first API type, based on the first contract, a second contract is generated by the first sidecar specifying a second API of the selected API type; and a second sidecar of the second microservice is caused to generate the second API and internally connect the second API to the first API based on the second contract.

公开(公告)号：US20230266419A1

公开(公告)日：2023-08-24

申请号：US18309366

申请日：2023-04-28

Applicant: Intel Corporation

Inventor： Yatish Mishra ,  Mats Agerstam ,  Mateo Guzman ,  Sindhu Pandian ,  Shubhangi Rajasekhar ,  Pranav Sanghadia ,  Troy Willes

IPC: G01R35/00 ,  G06N3/08 ,  H04L67/125 ,  G06Q10/04 ,  G06N20/00 ,  G06F16/903 ,  H04W4/50 ,  G06N3/04 ,  H04W4/70 ,  G06F18/214 ,  G06F18/2413 ,  G06V10/764 ,  H04W52/22

CPC classification number: G01R35/005 ,  G06N3/08 ,  H04L67/125 ,  G06Q10/04 ,  G06N20/00 ,  G06F16/90335 ,  H04W4/50 ,  G06N3/04 ,  H04W4/70 ,  G06F18/214 ,  G06F18/24143 ,  G06V10/764 ,  H04W52/223 ,  H04L67/12

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.