Sparkcognition, Inc.

Clearcube Technology, Inc.

Skygrid, LLC

Citrix Systems, Inc.

National Instruments Corporation

Syed Mohammad Amir Husain

Randy P Printz

Barry Thornton

Syed Mohammad Ali

Rajesh K Mellacheruvu

Todd John Enright

Zehra Akbar

Lowell L Duke

John Rutherford Allen

Sari Andoni

Keith D Moore

Taylor Schmidt

Martin Andreas Abel

Muhammad Umair

Qasim Iqbal

Michael Barron

Raymond A Dupont

Uses of artificial intelligence in battery technology including a method that includes receiving input data associated with at least one sensor. The method includes executing at least one trained model by a processor. Executing the trained model comprises providing the input data to the at least one trained model to generate a model output. The method further includes determining, based on the model output, whether to initiate or terminate selectively charging a first battery cell of a battery.

Systems and methods of applying artificial intelligence to battery technology

A method includes determining, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes obtaining sensor data distinct from the waveform data. The method further includes generating feature data based on the sensor data and the waveform data and providing the feature data as input to a first machine learning model and initiating a response action based on an output of the first machine learning model.

Machine-learning based analysis and response based on electromagnetic waveforms and sensor data

A method includes determining, based at least in part on parameters of a software-defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes generating feature data based on the waveform data and based on one or more symbols decoded from the electromagnetic waveform. The method further includes providing the feature data as input to a first machine-learning model and initiating a response action based on an output of the first machine-learning model.

Machine-learning based analysis and response to electromagnetic waveforms

Uses of artificial intelligence in battery technology including a method that includes receiving a trained model, receiving sensor data from at least one sensor associated with a battery, and executing the trained model by a processor. Executing the trained model includes providing the sensor data as input to the trained model to generate a model output. The method also includes sending, from the processor to a charge controller coupled to the battery, a control signal that is based on the model output and automatically, by the charge controller, initiating or terminating charging of the battery based on the control signal.

In a particular embodiment, coordinating an aerial search among unmanned aerial vehicles is disclosed that includes receiving, by a server in a UAV transportation ecosystem, search area data, accessing, by the server, UAV parameters for a type of UAV, determining, by the server in dependence upon the search area data and the UAV parameters, a number of UAVs needed to complete a coordinated aerial search of a search area within a time limit, and partitioning, by the server, the search area into a plurality of partitions, wherein the number of partitions is equal to the number of UAVs.

Coordinating an aerial search among unmanned aerial vehicles

Controlling the operation of one or more components in an electric vehicle, including: receiving, from one or more vehicle operation sensors, operation data including sensor data corresponding to a condition of one or more components of the vehicle; determining, using a trained model, whether the one or more components of the vehicle are operating in an acceptable manner; and generating a control signal to adjust operation of the one or more components of the vehicle.

Controlling the operation of a component in an electric vehicle

A method includes determining, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes generating feature data based on the waveform data. The method further includes providing the feature data as input to a first machine learning model to predict a future action of a device associated with at least a portion of the electromagnetic waveform and initiating a response action based on the predicted future action.

Machine-learning based analysis, prediction, and response based on electromagnetic waveforms

In a particular embodiment, decentralized oracles in an unmanned aerial vehicle (UAV) transportation ecosystem includes: receiving, by an oracle node of a plurality of oracle nodes, from a validating node of a plurality of validating nodes of a UAV transportation ecosystem, a request for data for fulfillment of a smart contract, wherein the plurality of oracle nodes are communicatively coupled via an overlay network implemented using a network communicatively coupling the plurality of validating nodes; generating, based on responses to the request from the plurality of oracle nodes, the data for fulfillment of the smart contract; and providing the data for fulfillment of the smart contract.

Decentralized oracles in an unmanned aerial vehicle (UAV) transportation ecosystem

A method includes selecting a subset of models from a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes performing at least one genetic operation of the genetic algorithm with respect to at least one model of the subset to generate a trainable model. The method includes determining a rate of improvement associated with prior backpropagation iterations. The method includes selecting, based on the rate of improvement, one of the trainable model or a prior trainable model as a selected trainable model. The method includes generating the trained model including training the selected trainable model. The method includes adding the trained model as input to a second epoch of the genetic algorithm that is subsequent to the first epoch.

Executing a genetic algorithm on a low-power controller

In a particular embodiment, blockchain-based hybrid authentication is disclosed that includes receiving, by a smart contract controller, an invocation of a smart contract and accessing, by the smart contract controller, one or more parameters associated with a client account. In this embodiment, the smart contract determines whether the one or more parameters fulfill the smart contract. In response to determining that the one or more parameters fulfill the smart contract, the smart contract provides an attestation of an identity associated with the client account.

Blockchain-based hybrid authentication

A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

Pre-processing for data-driven model creation

In a particular embodiment, parallel deconfliction processing of unmanned aerial vehicles (UAVs) is disclosed that includes a UAV control system partitioning the airspace over a geographic region into a plurality of airspace regions. In this example embodiment, the UAV control system includes a plurality of deconfliction controllers. For each deconfliction controller in the plurality of deconfliction controllers, the UAV control system assigns to the deconfliction controller, one or more airspace regions of the plurality of airspace regions. The UAV control system also inputs, in parallel, flight path data for a plurality of unmanned aerial vehicles (UAVs) to the plurality of deconfliction controllers. This embodiment also includes processing, in parallel by the plurality of deconfliction controllers, the flight path data to identify a flight conflict between two or more UAVs. Each deconfliction controller outputs a deconfliction result for each airspace region.

Parallel deconfliction processing of unmanned aerial vehicles

In a particular embodiment, recording data associated with an unmanned aerial vehicle (UAV) is disclosed that includes a blockchain manager receiving a transaction message associated with a UAV. The particular embodiment also includes the blockchain manager using the information within the transaction message to create a block of data. In this particular embodiment, the blockchain manager stores the created block of data in a blockchain data structure associated with the UAV.

Recording data associated with an unmanned aerial vehicle

Flight path deconfliction among unmanned aerial vehicles is disclosed. Telemetry data received from an unmanned aerial vehicle (UAV) and air traffic data received from a server or other data sources are analyzed, using deconfliction circuitry, to determine whether a flight path conflict indicative of a potential collision exists. The deconfliction circuitry reroutes the flight path of the UAV to avoid the flight path conflict, and transmits, in dependence upon the rerouted flight path, navigation instructions to the UAV for avoiding the potential collision. The deconfliction circuitry includes hardware-implemented logic optimized for processing the navigation data.

Flight path deconfliction among unmanned aerial vehicles

A method includes obtaining route information at an unmanned aerial vehicle (UAV). The route information indicates a plurality of zones between an origin and a destination and a corresponding set of mobile devices for each zone. The method includes receiving first control data at the UAV from a first mobile device while in a first zone of the plurality of zones. The first mobile device is included in a first set of mobile devices corresponding to the first zone. The method further includes receiving second control data at the UAV from a second mobile device while in a second zone of the plurality of zones. The second mobile device is included in a second set of mobile devices corresponding to the second zone.

Growing community of inventors

Round Rock, TX, United States of America

Syed Mohammad Amir Husain