Intel Corporation

Apple Inc.

Maruti Gupta Hyde

Srikathyayani Srikanteswara

Eve M Schooler

Nageen Himayat

Satish Chandra Jha

Hassnaa Moustafa

Venkatesan Nallampatti Ekambaram

Ravikumar Balakrishnan

Rath Vannithamby

Vallabhajosyula S Somayazulu

Jessica C McCarthy

Van H Le

Hyung-Nam Choi

Shahrnaz Azizi

Marta Martinez Tarradell

Min Suet Lim

Timothy F Cox

Ralph Hasholzner

Ned M Smith

Kathiravetpillai Sivanesan

Qiaoyang Ye

Ali Taha Koc

Dave A Cavalcanti

Feng Xue

Rajashree Raji Baskaran

Yiting Liao

Ajay G Gupta

Thomas J Kenney

Markus Dominik Mueck

Bernhard Raaf

Shilpa Talwar

Christian Maciocco

Christian Drewes

Zhibin Yu

Thorsten Clevorn

John Joseph Browne

Shao-Wen Yang

Robert Zaus

Biljana Badic

Deepak Shashidhar Vembar

Simon R Hunt

Katalin Klara Bartfai-Walcott

Mohammad Mamunur Rashid

Yen-kuang Chen

S M Iftekharul Alam

Ingolf Karls

Juergen H Kreuchauf

Yi Zhang

Arun Raghunath

Stefan Franz

Reinhold Schneider

Christopher MacNamara

Sarabjot Singh

Niall Power

Christina R Strong

Ashish Singh

Seunghee Han

Debdeep Chatterjee

Bharat Shrestha

Jing Zhu

Puneet K Jain

Salvatore Talarico

Sangeetha L Bangolae

Ana Lucia Pinheiro

Alexander Bachmutsky

Sebastian Schoenberg

Gabriel Arrobo Vidal

Jeffrey Sedayao

Marcin Spoczynski

Johannes Brendel

Francesc Guim Bernat

Kshitij Arun Doshi

Minyoung Park

Ariela Zeira

Xintian Eddie Lin

Ren Wang

Ulun Karacaoglu

Linda L Hurd

Alexander W Min

Atsuo Kuwahara

Xue Yang

Javier Perez-Ramirez

Kuilin Clark Chen

Vesh Raj Sharma Banjade

Jie Gao

Florence R Pon

Wei Yee Koay

Roya Doostnejad

Vinod Kristem

Moreno Ambrosin

Mikhail T Galeev

David John Zage

Eduardo Alban

Mi Sun Park

Mustafa Akdeniz

Stepan Karpenko

Andrew Stephen Brown

Gayathri Jeganmohan

Arjun Anand

Radhika Loomba

In one embodiment, a node of a data centric network (DCN) may receive a first service request interest packet from another node of the DCN, the first service request interest packet indicating a set of functions to be performed on source data to implement a service. The node may determine that it can perform a particular function of the set of functions, and determine, based on a backlog information corresponding to the particular function, whether to commit to performing the particular function or to forward the service request interest packet to another node. The node may make the determination further based on service delivery information indicating, for each face of the node, a service delivery distance for implementing the set of functions.

Queuing control for distributed compute network orchestration

The present disclosure relates to a system for use in a wireless network, the system including: a processor configured to determine a performance parameter representative of a performance of a model of radio resource management, the model operating on a radio access network environment; and a radio resource manager configured to perform radio resource management dependent on the performance parameter.

Radio resource model management in a wireless network

Systems and techniques for cross-layer automated fault tracking and anomaly detection are described herein. Anomaly data may be obtained from a plurality of layers of a network. Elements of the anomaly data may be identified that correspond to a data flow of an application executing on the network. An artificial intelligence model may be trained using the elements of the anomaly data to generate an impact score for the application. The impact score may be generated for the application by evaluating current network metrics using the artificial intelligence model. An operational component of the network may be modified based on the impact score.

Cross-layer automated fault tracking and anomaly detection

In one embodiment, a road side unit (RSU) establishes a data offload session with a vehicle in the vicinity of the RSU based on a session establishment request sent by the vehicle, and stores data received from the vehicle during the data offload session in its memory. The RSU generates storage record information (including identifying information for the RSU) for the stored data, and transmits the storage record information to the vehicle.

Data offload and time synchronization for ubiquitous visual computing witness

A method of implementing distributed AI or ML learning for autonomous vehicles is disclosed. An AI or ML model specific to a location or a type of the location is generated at a vehicle. In response to a detection that the vehicle is within a proximity to a road side unit (RSU) associated with the location or the type of the location or the vehicle is within a proximity to an additional vehicle that is present or anticipated to be present at the location or the type of the location, causing an AI or ML model transmission to the additional vehicle or the RSU. Based on the causing the AI or ML model reception, causing deployment of an additional AI or ML model in the vehicle to optimize the vehicle for the location or the type of the location.

Distributed learning to learn context-specific driving patterns

A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Methods and devices for radio communications

The present disclosure may be directed to a computer-assisted or autonomous driving (CA/AD) vehicle that receives a plurality of indications of a condition of one or more features of a plurality of locations of a roadway, respectively, encoded in a plurality of navigation signals broadcast by a plurality of transmitters as the CA/AD vehicle drives past the locations enroute to a destination. The CA/AD vehicle may then determine, based in part on the received indications, driving adjustments to be made and send indications of the driving adjustments to a driving control unit of the CA/AD vehicle.

Computer-assisted or autonomous driving assisted by roadway navigation broadcast

A method can be performed by a first node for determining a parameter of physical (PHY) layer circuitry of a second node. The method can include implementing a cascaded hierarchy of techniques to determine, based on an electrical signal from a second node, a parameter of the PHY layer circuitry of the second node, and causing an antenna of the first node to transmit an electromagnetic wave consistent with the determined parameter.

Feature detection in collaborative radio networks

Methods, apparatus, systems, and articles of manufacture are disclosed that generate dynamic latency values. An example apparatus includes an active status controller to determine that a modem is active based on a number of packets obtained from a network, a prediction controller to predict that the number of packets are indicative of a workload type based on a trained model, and a latency value generator to generate a latency value based on the workload type of the number of packets, the latency value to cause a processor processing the number of packets to enter a power saving state or a power executing state.

Methods and apparatus to generate dynamic latency messages in a computing system

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Ubiquitous witness for vehicle incidents

In various aspects, power saving host-modem interactions are described herein. For instance, a host of a communication device includes a host processor that receives, via an interface, information about the reduced activity state of a modem of the communication device including an indication of the RRC state of the modem. It also receives, via the interface, information about a projected expiration of a reduced activity period of the modem including an indication of a time remaining until a next event of the modem. The host processor controls a reduction of an operation of a component of the communication device during the reduced activity period of the modem, based on the indication of the RRC state of the modem and the indication of the time remaining until the next event of the modem. The reduced activity state of the modem corresponding to the reduced activity period of the modem.

Power saving host-modem interaction in a communication device

Systems and methods of reducing power consumption associated with paging or cDRX mode are described. A wake-up receiver (WUR) wakes up from an idle mode or cDRX state. Whether a wake-up signal (WUS) has been received by the WUR is determined. The WUS is a low-complexity signal that is less complicated than a PDCCH or PDSCH and is repeated multiple times at resource elements as indicated in a configuration from an eNB. If received, a baseband transceiver wakes up for reception of a PDCCH for the UE in a PO when the UE is in the idle mode or a PDSCH for the UE when the UE is in the cDRX state.

Wake up signal for machine type communication and Narrowband-Internet-of-Things devices

Methods and apparatus to provide power management for multi-die stacks using artificial intelligence are disclosed. An example integrated circuit (IC) package includes a computer processor unit (CPU) die, a memory die, inference engine circuitry within the CPU die, the inference engine circuitry to infer, based on a first machine learning model, a workload for at least one of the CPU die or the memory die, and power management engine circuitry within the CPU die, the power management engine circuitry distinct from the inference engine circuitry, the power management engine circuitry to adjust, based on a second machine learning model different than the first machine learning model, operational parameters associated with the at least one of the CPU die or the memory die, the inferred workload to be an input to the second machine learning model.

Multi-die stacks with power management

A wireless communication device includes a processor configured to select an offload processing task for performance by an edge computing device; cause a baseband modem to establish a direct wireless connection between the wireless communication device and the edge computing device; cause the baseband modem to send first data to the edge computing device via the direct wireless connection; and receive second data from the edge computing device, wherein the second data comprise a result of the offload processing task performed on the first data. The edge computing device includes a processor configured to receive, from a user device, offloaded data to be processed according to an offload processing task; execute the offload processing task on the offloaded data; and cause the radio via the interface to wirelessly send a result of the executed offload processing task via a direct wireless connection with the user device.

A telecommunication network may operate to enable a wake up signals (WUSs) within the telecommunication network. A mobility management entity (MME) may estimate a coverage enhancement (CE) level of a user equipment (UE), determine, based on the CE level, a number of repetitions for a wake up signal (WUS) for the UE, and cause a WUS for UE to be transmitted to a radio access network (RAN) node corresponding to the UE. The RAN node may inform the MME that the RAN node has disabled the WUS feature, and may cause system information to be broadcast to UEs in IDLE mode, indicating that the WUS feature of the RAN node has been disabled. A UE may determine a paging occasion (PO) determine a maximum WUS duration, minimum offset, and start location of the WUS.

Wake up signaling in wireless telecommunication networks

Growing community of inventors

Portland, OR, United States of America

Maruti Gupta Hyde