Intel Corporation

Mateo Guzman

Marcos Emanuel Carranza

Cesar Martinez-Spessot

Mats Gustav Agerstam

Dario Oliver

Francesc Guim Bernat

Sindhu Pandian

Javier Felip Leon

Javier Sebastian Turek

Lakshmi N Talluru

Kshitij Arun Doshi

Rajesh Poornachandran

Karthik Kumar

Yatish Mishra

Pranav Sanghadia

Shubhangi Rajasekhar

Troy Willes

Mariano Tepper

Cesar I Martinez Spessot

Sebastian M Salomon

Rita H Wouhaybi

Satish Chandra Jha

Ignacio Javier Alvarez

David Israel Gonzalez Aguirre

S M Iftekharul Alam

Jose Rodrigo Camacho Perez

Paulo Lopez Meyer

Hector A Cordourier Maruri

Javier Perez-Ramirez

Julio Cesar Zamora Esquivel

Jesus Cruz Vargas

Ignacio J Alvarez Martinez

Vy Vo

Marocs Carranza

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

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 herein are systems and methods for vehicle-occupancy-based and user-preference-based smart routing, and autonomous volumetric-occupancy measurement. In an embodiment, a system is configured to receive from a user device associated with a user, a routing-options request for routing options between two locations, and to responsively identify one or more routing options between the two locations based at least in part on occupancy data for a vehicle that would be utilized for at least a portion of at least one of the identified routing options. The occupancy data is based on an output of an automated occupancy-measurement system onboard the vehicle. The system is also configured to provide the one or more identified routing options to the user device. In some embodiments, the occupancy data is obtained using volumetric-occupancy measurement. Some embodiments relate to volumetric-occupancy measurement conducted by autonomous mesh nodes.

Systems and methods for vehicle-occupancy-based and user-preference-based smart routing and autonomous volumetric-occupancy measurement

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.

Dynamic application programming interface (API) contract generation and conversion through microservice sidecars

Embodiments described herein are generally directed to a transparent and adaptable mechanism for performing secure application communications through sidecars. In an example, a set of security features is discovered by a first sidecar of a first microservice of multiple microservices of an application. The set of security features are associated with a device of multiple devices of a set of one or more host systems on which the first microservice is running. Information regarding the set of discovered security features is made available to the other microservices by the first sidecar by sharing the information with a discovery service accessible to all of the microservices. A configuration of a communication channel through which a message is to be transmitted from a second microservice to the first microservice is determined by a second sidecar of the second microservice by issuing a request to the discovery service regarding the first microservice.

Secure application communications through sidecars

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.

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

Systems, apparatuses and methods may provide for origination camera technology that generates a cell representation of a local space associated with an origination camera in a multicast domain, predicts that an object in the local space will exit the local space and enter one or more adjacent spaces associated with additional cameras in the multicast domain, and sends the cell representation and a trajectory of the object to the additional cameras before the object exits the local space. Additionally, transition camera technology may generate a leader election message based on a multicasted trajectory of an object and a predicted trajectory of the object, send the leader election message from a transition camera to one or more additional cameras in a multicast domain, and track the object in the local space in response to a leader notification message.

Object tracking technology based on cognitive representation of a location in space

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.

Camera orchestration technology to improve the automated identification of individuals

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.

Self-supervised learning system for anomaly detection with natural language processing and automatic remediation

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.

Artificial imagination engine using 5D information descriptors

Object tracking and identification using intelligent camera orchestration

Growing community of inventors

Beaverton, OR, United States of America

Mateo Guzman