Microsoft Technology Licensing, LLC

Elwha LLC

AT&T Intellectual Property I, LP

At+t Corp.

Microsoft Corporation

Ranveer Chandra

Paramvir Bahl

Thomas Moscibroda

Nissanka Arachchige Bodhi Priyantha

Anirudh Badam

Deepak Vasisht

Lowell L Wood, Jr

Roderick A Hyde

Clarence T Tegreene

Jordin T Kare

Nathan P Myhrvold

Robert W Lord

Muriel Y Ishikawa

Victoria Y H Wood

Charles Whitmer

Richard T Lord

William Henry Gates, Iii

Douglas Christopher Burger

Desney S Tan

Craig J Mundie

Matthew Graham Dyor

Lin Zhong

Tim Paek

Stephen Edward Hodges

Alastair Wolman

Pablos Holman

Mark A Malamud

Royce A Levien

John D Rinaldo, Jr

Jitendra Dattatraya Padhye

Ashish Kapoor

Paul D Holman

Yunnan Wu

Sean Michael James

Sudipta Narayan Sinha

David W Russo

Yongguang Zhang

Lili Qiu

Brian Don Zill

David Thomas Gauthier

Eric J Horvitz

Jie Jerry Liu

Eric Clarence Peterson

Atul Adya

Ming Zhang

Stefan Saroiu

Srikanth Kandula

Stanley William Adermann

James William Scott

Edmund Bernard Nightingale

Kun Tan

David A Maltz

Mark William Casebolt

Jiansong Zhang

Thomas W Kuehnel

Albert Gordon Greenberg

John Charles Krumm

Qi Zhang

Shivkumar Kalyanaraman

Sudipta Sengupta

Venkata N Padmanabhan

Kamal Jain

Dinei A Florencio

Bozidar Radunovic

Chandramohan Amyangot Thekkath

Bruce Lee Worthington

Ratul Mahajan

Rahul Anand Sharma

Jonathan Alan Dutra

Yunxin Liu

John Mark Miller

Humayun Mukhtar Khan

Dinan Srilal Gunawardena

Gursharan Singh Sidhu

Mohammad Mahdian

Yong He

Philip Andrew Chou

Perry Victor Lea

Karin Strauss

Mark Alan Champion

Kentaro Toyama

Sharad Agarwal

Ivan Jelev Tashev

Feng Zhao

Pradeep Bahl

Ramachandran Ramjee

John David Dunagan

Mark Eugene Russinovich

Bichlien Hoang Nguyen

Aman Kansal

Vishal Vikram Ghotge

Paul William Garnett

Antony Ian Taylor Rowstron

Christos Gkantsidis

Kamran Rajabi Zargahi

Yinglian Xie

Eric I Horvitz

Rohan Narayan Murty

Yuan-Jyue Chen

Manikanta Kotaru

Recycling information is associated with objects through the use of molecular tags. The recycling information may describe the type of material that the object is made from as well as provide instructions for recycling. The molecular tags may be polynucleotides or other types of molecules including inorganic molecules. The molecular tags may be embedded within the object or attached to the surface of the object. At the end of the object's life, the molecular tags are read and the recycling information is used to appropriately recycle the object.

Molecular encoding of recycling information

A computing device including a processor configured to receive satellite status data from satellites included in a satellite constellation. The processor is further configured to determine a link topology of the satellites. Based at least in part on the satellite status data and the link topology, the processor is further configured to identify a first satellite constellation subset including one or more selected satellite pairs. Identifying the one or more selected satellite pairs includes computing respective link utility values associated with a plurality of candidate pairs of satellites included in the satellite constellation based at least in part on the satellite status data and the link topology. The one or more selected satellite pairs are selected based at least in part on the link utility values. The processor is further configured to transmit, to the satellites included in the first satellite constellation subset, instructions to perform intersatellite imaging data transfer.

Intersatellite imaging data transfer

The disclosure described herein configures a client device for communication using dynamic spectrum access within a frequency spectrum, such as television white space (TVWS), using a determined location of the client device based on location information, such as from a global positioning system. A dynamic spectrum access database of channels is accessed based on the location information. Available channels are determined for the client device from the channels based on the location information. A list of the available channels for use by the client device are transmitted to the client device, thereby allowing narrowband communication over the channels.

Communication using dynamic spectrum access based on channel selection

Securing and optimizing communications for a cloud service provider includes collecting connection summary information at network interface devices associated with host computing devices for a group of resources allocated to a customer of the cloud computing environment. The connection summary information includes local address information, remote address information, and data information, each connection established via the network interface devices. At least one communication graph is generated for the group of resources using the connection summary information. The graph includes nodes that represent communication resources of the group of resources and edges extending between nodes that characterize communication between the nodes. At least one analytics process is performed on data from the graph to identify at least one of a micro-segmentation strategy, a communication pattern, and a flow prediction for the group of resources.

Communication visualization and analytics system for public clouds

A deployment of sensors transmit radio frequency (RF) signals into an area of interest. The radar maps are generated from the reflected signals, including a static radar map and a dynamic radar map. Multipath and radar sidelobes are removed from the radar maps using a neural network to produce a density map. The neural network can be trained in two phases: a training phase that uses training data from a training site and a transfer learning phase that uses training data from the area of interest.

Radio frequency-based crowd analytics

A computing system including a processor configured to receive training data including, for each of a plurality of training timesteps, training forecast states associated with respective training-phase agents included in a training supply chain graph. The processor may train a reinforcement learning simulation of the training supply chain graph using the training data via policy gradient reinforcement learning. At each training timestep, the training forecast states may be shared between the training-phase agents during training. The processor may receive runtime forecast states associated with respective runtime agents included in a runtime supply chain graph. For a runtime agent, at the trained reinforcement learning simulation, the processor may generate a respective runtime action output associated with a corresponding runtime forecast state of the runtime agent based at least in part on the runtime forecast states. The processor may output the runtime action output.

Reinforcement learning simulation of supply chain graph

The description relates to context-aware management of satellite images. One example can track a satellite relative to locations on earth, ground stations, and other satellites and identify parameters associated with the tracked satellite. The example can manage images captured by the satellite for individual locations based upon the identified parameters.

Satellite image management

Examples are disclosed that relate to fairly ordering financial market trades received from different market participant computers via a cloud computing network. In one example, a plurality of trades generated by a plurality of market participant computers are received. The trades are generated based at least on a financial market data point received by the plurality of market participant computers. Each trade is tagged with a delivery clock time stamp that tracks time in relation to financial market events that occur at a corresponding market participant computer. The trades are ordered based on the delivery clock time stamps and sent to a central exchange server computer. The central exchange server computer processes the trades.

Response-time-based ordering of financial market trades

A computing system including a processor configured to receive packet preamble binary data and packet header binary data associated with a satellite. The processor may generate a simulated signal that encodes the packet preamble binary data and the packet header binary data. The processor may receive a satellite downlink signal. Within each of a plurality of sample intervals of the satellite downlink signal, the processor may compute a respective correlation between the satellite downlink signal and at least a portion of the simulated signal. The processor may select an identified sample interval of the plurality of sample intervals based at least in part on the plurality of correlations. The processor may decode binary satellite signal data based at least in part on the identified sample of the satellite downlink signal. The processor may output the binary satellite signal data.

Correlating simulated signal and satellite downlink signal

A method of managing wireless communication between electronic devices includes, at a first electronic device, transmitting a first downlink transmission from the first electronic device to a second electronic device and receiving a first response from the second electronic device at the first electronic device, wherein the first response includes a downlink channel quality information (CQI). The method further includes determining a per-subcarrier CQI based at least partially on the downlink CQI and determining an output modulation and coding scheme (MCS) based at least partially on the per-subcarrier CQI. After determining the output MCS, the method includes selecting a selected MCS based at least partially on the output MCS and setting an MCS of the first electronic device to the selected MCS.

Systems and methods for proactive wireless communication rate adaptation

The disclosure herein describes transmitting data from a satellite using a ground station configured to both transmit and receive, and a set of ground stations configured to receive data from a satellite. An orbit of the satellite is determined over a schedule period and subset of ground stations configured to receive data and not transmit data to satellite is identified based on the determined orbit of satellite. A transmission schedule associated with the satellite is then generated. For each ground station of the subset, a time interval during which satellite is within communication range is determined, an expected transmission rate is estimated, and time interval and expected transmission rate are included in transmission schedule. The transmission schedule is provided to the satellite via another ground station configured to both transmit and receive, whereby the satellite is configured to transmit data to the subset of ground stations based on transmission schedule.

Scheduling satellite data transmissions using differing sets of ground stations

The techniques disclosed herein enable systems to enable multi-market optimization of renewable energies using data-driven models. To achieve this, a model retrieves a current state from a resource generation system and associated resource markets. The model can then compute a policy based on the state as well physical and technical constraints. The policy defines various actions that direct operation of the resource generation system such as resource production and dispatch to markets. Applying the policy to the resource generation results in a modified state which the model extracts along with a measure of optimality which quantifies the success of the policy. Based on these metrics, the model can generate an updated iteration of the policy defining a different set of actions. In this way, the model can gradually develop an optimal policy for controlling the resource generation system.

Multi-market resource dispatch optimization

A device and method for managing communication frequencies, including generating a geographical grid with a plurality of cells and allocating communication frequencies to the cells. Each of the communication frequencies is used for communication between a satellite and a ground device or ground IoT modem. A communication frequency allocated to one cell is different from the communication frequencies allocated to each of the immediately surrounding cells. A set of communication frequencies is selected for communication between the satellite and ground devices based on a ground track of the satellite. The selected set is transmitted to the satellite to communicate with the ground station located in a cell with the communication frequency allocated to that cell.

Spectrum management for satellite constellations

The disclosure herein describes using satellites and ground sinks and/or stations for routing IoT device data packets from IoT devices. A target ground sink in range of the satellite is identified and an expected reception (ER) score for the target ground sink is calculated based on ER parameter data and location data of the satellite. A data packet in a first level of a multi-level data structure of the satellite is sent to the target ground sink and, based on an ER threshold exceeding the ER score, the packet is moved to a second level of the multi-level data structure, whereby the data packet is queued to be sent to another ground sink. The disclosure further includes using cell towers as ground sinks and/or using them for backhauling with other ground sinks. The flexibility of the disclosure enables large ground sink networks to be established, reducing latency of packet routing.

Satellite communication with distributed ground stations using a multi-level queue

A computing system including an edge computing device. The edge computing device may include an edge device processor configured to receive edge device contextual data including computing resource availability data. Based at least in part on the edge device contextual data, the edge device processor may select a processing stage machine learning model of a plurality of processing stage machine learning models and construct a runtime processing pipeline of one or more runtime processing stages including the processing stage machine learning model. The edge device processor may receive a runtime input, and, at the runtime processing pipeline, generate a runtime output based at least in part on the runtime input. The edge device processor may generate runtime pipeline metadata that indicates the one or more runtime processing stages included in the runtime processing pipeline. The edge device processor may output the runtime output and the runtime pipeline metadata.

Constructing processing pipeline at edge computing device

Growing community of inventors

Kirkland, WA, United States of America

Ranveer Chandra