Intel Corporation

Ignacio J Alvarez Martinez

Ned M Smith

Satish Chandra Jha

Mark Y Kelly

Robert W Adams

Shao-Wen Yang

David W Browning

Yen-kuang Chen

Michael J Nolan

S M Iftekharul Alam

Marcos Emanuel Carranza

Vesh Raj Sharma Banjade

John Brady

Arvind Merwaday

Helen Adrienne Frances Gould

Francesc Guim Bernat

Kathiravetpillai Sivanesan

Mats Gustav Agerstam

Kuilin Clark Chen

Cesar Martinez-Spessot

Mateo Guzman

Sindhu Pandian

Dario Oliver

Tung Lun Loo

Juan Pablo Munoz

Julio Jarquin Arroyo

Chia Chuan Wu

Wei Seng Yeap

Charmaine Rui Qin Chan

Xiaoyun May Wu

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

Disclosed herein are systems and methods for cross-domain training of sensing-system-model instances. In an embodiment, a system receives, via a first application programming interface (API), an input-dataset selection identifying an input dataset, which includes a plurality of dataframes that are in a first dataframe format and that have annotations corresponding to one or more sensing tasks performed with respect to the dataframes. The system executes a plurality of dataframe-transformation functions to convert the plurality of dataframes of the input dataset into a predetermined dataframe format. The system trains an instance of a first machine-learning model using the converted dataframes of the input dataset to perform at least a subset of the one or more sensing tasks. The system outputs, via the first API, one or more model-validation metrics pertaining to the training of the instance of the first machine-learning model.

Systems and methods for cross-domain training of sensing-system-model instances

System and techniques for abandoned object detection are described herein. a fence is established about a person and an object is detected within the fence. An entry is created in an object-person relationship data structure to establish a relationship between the person and the object within the fence. Then, the position of the object is monitored until an indication that the fence is terminated is received. If it is detected that the object is outside the fence during the monitoring, the person is alerted.

Abandoned object detection

Various systems and methods for implementing an assessment and response mechanism for autonomous systems are described herein. An assessment and response system for an autonomous system is configured to access a realm classification of an event; determine a hazard score based on the realm classification, a severity metric, a likelihood metric, an urgency metric, and a confidence level metric; identify, based on the hazard score, a responsive action; and record details of the hazard score determination and the responsive action in a decision ledger.

Assessment and response mechanism for autonomous systems

Systems and techniques for mobility-as-a-service for user experience are described herein. An orchestration log may be maintained that includes current orchestration data. An orchestration backup record may be generated that includes alternate MaaS nodes on the MaaS network. It may be determined that connectivity is lost to a first orchestration container hosted by a first MaaS node. An orchestration container is generated using the orchestration log to maintain orchestration functionality. An available second MaaS node is identified from the alternate MaaS nodes. The orchestration container may be transferred to the second MaaS node.

Mobility-as-a-service for user experience

Various systems and methods implement a decision architecture for autonomous systems. An autonomous system framework includes a safety ring configured to interface with safety features of an autonomous system; a security ring configured to provide authentication and verification services to transactions passed through the security ring; a privacy ring configured to ensure privacy of a user of the autonomous system; a trustworthiness ring configured to log and provide transparency of transactions passed through the trustworthiness ring; and a well-being ring configured to interface with the user and provide feedback and information to the user on a state of the autonomous system, wherein: each of the safety, security, privacy, trustworthiness, and well-being rings include at least one interface to at least one other of the safety, security, privacy, trustworthiness, and well-being rings.

Decision architecture for autonomous systems

Disclosure herein are systems and methods for dynamically adjusting infrastructure items, such as street lights, construction signage, and/or other lighting elements. The systems and methods may include receiving environmental data for a sector containing the infrastructure items. A quality of infrastructure effectors located within the sector may be determined. A deviation from a standard infrastructure quality associated with the infrastructure effectors may be determined. A setting of the infrastructure items located in the sector may be changed to minimize the deviation from the standard infrastructure quality.

Dynamically adjusting an infrastructure item

Various systems and methods for improving connectivity of a Mobility-as-a-Service (MaaS) node are described herein, including categorizing MaaS communication traffic of a MaaS node into different levels of priority and controlling duplication or repetition of the MaaS communication traffic using the categorized level of priority of the MaaS communication traffic.

Always-on mobility-as-a-service connectivity

Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Distributed framework for resilient machine-to-machine system management

Growing community of inventors

Portland, OR, United States of America

Ignacio J Alvarez Martinez