Intel Corporation

Mats Gustav Agerstam

Ned M Smith

Marcos Emanuel Carranza

Vijay Sarathi Kesavan

Robert Colby

Nathan Heldt-Sheller

Mateo Guzman

Francesc Guim Bernat

Justin E Gottschlich

Maria E Ramirez Loaiza

Kshitij Arun Doshi

Rajesh Poornachandran

John Joseph Browne

Cesar Martinez-Spessot

Alexander F Heinecke

Jaideep Moses

Venkata R Vallabhu

David J McCall

Kapil Sood

Susanne M Balle

Mark D Yarvis

Karthik Veeramani

Katalin Klara Bartfai-Walcott

Alexander Bachmutsky

Dario Sabella

Tarun Viswanathan

Rajneesh Chowdhury

Abhilasha Bhargav-Spantzel

Shengtian Zhou

Thijs Metsch

Mohammad Mejbah Ul Alam

Xintian Eddie Lin

Jing Zhu

Jesse R Walker

Bahareh Sadeghi

Sara S Baghsorkhi

Srikathyayani Srikanteswara

Jaroslaw Sydir

Jason Martin

Ravi S Subramaniam

Ignacio J Alvarez Martinez

Anthony G Lamarca

Shilpa A Sodani

Douglas K Hudson

Thuyen C Tran

Sindhu Pandian

Dario Oliver

Patrick C Lankswert

Yatish Mishra

Pranav Sanghadia

Shubhangi Rajasekhar

Troy Willes

Sebastian M Salomon

Bin Li

Adrian C Hoban

Mikko Ylinen

Sachin Agrawal

Krishna Surya

Lakshmi N Talluru

Roy Ramon

Cesar I Martinez Spessot

Brian Cremeans

Amir Shenhav

David Cremins

Benjamin Walker

Jeffrey Ota

Eduardo Cabre

Raz Weizman

Raguraman Barathalwar

Patrick Koeberl

Kirk Skeba

John Mangan

Joseph Morrow

Narasimham Gadiraju

Assaf Mevorach

Rajesh Poorchandran

Sashi Kumar Penta

Robert A Colby

System and techniques for hands-free deployment of geographically linked applications are described herein. A radio transceiver of a vehicle may detect a beacon. Here, the beacon includes an indication that an application linked to a geographical area covered by the beacon is available. The vehicle may establish a secure communications link with an application server corresponding to the beacon and receive, through the secure communications link, an application. Then, the vehicle may execute the application in a protected execution environment without intervention by a user of the vehicle.

Hands-free deployment of geographically linked applications

Various systems and methods for establishing network connectivity and onboarding for Internet of Things (IoT) devices and trusted platforms, including in Open Connectivity Foundation (OCF) specification device deployments, are discussed. In an example, a zero touch owner transfer method includes operations of: receiving a first request from a new device for network access to begin an onboarding procedure with a network platform; transmitting credentials of a first network to the new device, the first network used to access a rendezvous server and obtain onboarding information associated with the network platform; receiving a second request from the new device for network access to continue the onboarding procedure; and transmitting credentials of a second network to the new device, as the new device uses the second network to access the onboarding server of the network platform and perform or complete the onboarding procedure with the network platform.

Secure device onboarding techniques

Various systems and methods for implementing intent-based cluster administration are described herein. An orchestrator system includes: a processor; and memory to store instructions, which when executed by the processor, cause the orchestrator system to: receive, at the orchestrator system, an administrative intent-based service level objective (SLO) for an infrastructure configuration of an infrastructure; map the administrative intent-based SLO to a set of imperative policies; deploy the set of imperative policies to the infrastructure; monitor performance of the infrastructure; detect non-compliance with the set of imperative policies; and modify the administrative intent-based SLO to generate a revised set of imperative policies that cause the performance of the infrastructure to be compliant with the revised set of imperative policies.

Intent-based cluster administration

Apparatus, devices, systems, methods, and articles of manufacture for intent-based machine programming are disclosed. An example system categorize source code blocks includes a code repository accessor to access a code repository and select a source code block. The example system also includes a signature generator to generate a signature for the source code block, a collateral miner to extract collateral associated with the source code block, and a tokenizer to transform the source code block into tokens. In addition, the example system includes a function assessor to determine a function of the source code block based on the collateral and the tokens and an input/output determiner to determine an input and an output of the source code block based on the collateral and the signature. The example system further includes a tagger to categorize the source code block with the function, input, and output.

Intent-based machine programming

Various systems and methods for implementing intent-based orchestration in heterogenous compute platforms are described herein. An orchestration system is configured to: receive, at the orchestration system, a workload request for a workload, the workload request including an intent-based service level objective (SLO); generate rules for resource allocation based on the workload request; generate a deployment plan using the rules for resource allocation and the intent-based SLO; deploy the workload using the deployment plan; monitor performance of the workload using real-time telemetry; and modify the rules for resource allocation and the deployment plan based on the real-time telemetry.

Intent-based orchestration in heterogenous compute platforms

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.

Methods and apparatus to trigger calibration of a sensor node using machine learning

Disclosed in some examples are methods, systems, and machine readable mediums for secure, low end-user effort computing device configuration. In some examples the IoT device is configured via a user's computing device over a short range wireless link of a first type. This short range wireless communication may use a connection establishment that does not require end-user input. For example, the end user will not have to enter, or confirm a PIN number or other authentication information such as usernames and/or passwords. This allows configuration to involve less user input. In some examples, to prevent man-in-the-middle attacks, the power of a transmitter in the IoT device that transmits the short range wireless link is reduced during a configuration procedure so that the range of the transmissions to and from the user's computing device are reduced to a short distance.

Range constrained device configuration

Methods, apparatus, systems and articles of manufacture are disclosed to identify and manage IoT protocols and associated devices. An example apparatus includes a gateway device to communicate according to a first protocol. The example gateway device includes a plugin agent to discover a first device and probe the first device to gather data regarding a protocol of the first device. The example plugin agent is to transmit the gathered data to a plugin manager to determine whether the first device is to communicate via the first protocol, and, when the first device is unable to communicate via the first protocol, determine a plugin for the gateway device to enable the gateway device to communicate with the first device, the plugin agent to provision the plugin for the gateway device with respect to the first device.

Framework for IoT protocol identification and management

A service coordinating entity device includes communications circuitry to communicate with a first access network, processing circuitry, and a memory device. The processing circuitry is to perform operations to, in response to a request for establishing a connection with a user equipment (UE) in a second access network, retrieve a first Trusted Level Agreement (TLA) including trust attributes associated with the first access network. One or more exchanges of the trust attributes of the first TLA and trust attributes of a second TLA associated with the second access network are performed using a computing service executing on the service coordinating entity. A common TLA with trust attributes associated with communications between the first and second access networks is generated based on the exchanges. Data traffic is routed from the first access network to the UE in the second access network based on the trust attributes of the common TLA.

Multi-domain trust establishment in edge cloud architectures

Technologies for context-aware dynamic bandwidth allocation include a network compute device configured to collect context inputs from a plurality of compute devices communicatively coupled to the network compute device. The network compute device is further configured to identify a context of each compute device based on the collected context inputs and determine a bandwidth priority for each compute device based on the identified context. Additionally, the network compute device is configure to determine an amount of bandwidth from a total available bandwidth to allocate to the compute device based on the determined bandwidth priority and update a moderated bandwidth allocation policy to reflect the determined amount of bandwidth allocated to the compute device. Other embodiments are described herein.

Technologies for context-aware dynamic bandwidth allocation

In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of cameras. The processor is to obtain metadata associated with an initial state of an object, wherein the object is captured by a first camera in a first video stream at a first point in time, and wherein the metadata is obtained based on the first video stream. The processor is further to predict, based on the metadata, a future state of the object at a second point in time, and identify a second camera for capturing the object at the second point in time. The processor is further to configure the second camera to capture the object in a second video stream at the second point in time, wherein the second camera is configured to capture the object based on the future state of the object.

Person tracking and identification using intelligent camera orchestration

Various systems and methods for network optimization or bandwidth conservation may use plugin migration or mirroring to access a plugin utilizing a first network protocol in the cloud. A cloud-based plugin allows for routing optimization to leverage resource directory from the first network protocol to provide discovery or access to the plugin. The plugin may be used when a device operating the first network protocol communicates with a device operating a second, different, network protocol.

Plugin management for internet of things (IoT) network optimization

Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Methods and apparatus to provide machine assisted programming

Growing community of inventors

Portland, OR, United States of America

Mats Gustav Agerstam