International Business Machines Corporation

Justin Gregory Manweiler

Justin David Weisz

Katherine Vogt

Umut Topkara

Yu Ma

Gregory F Boland

Kevin E Siemonsen

James R Kozloski

Ashish Jagmohan

Hani Talal Jamjoom

David M Lubensky

Kirk Alan Beaty

Arup Acharya

Mahanth K Gowda

Puneet Jain

Krishna Chaitanya Ratakonda

Yi-Min Chee

Mathew Wiesman

Alex Pomerenk

Joshua Pitkofsky

Saad Ismail

Eric Rozner

James Xenidis

Anni Rosa Coden

Jan Simon Rellermeyer

An example operation may include one or more of receiving a data file submitted by a node, the data file comprising information about an event-driven process for a chain of nodes, retrieving an entitlement mode of the data file from a data block that is stored among a hash-linked chain of data blocks on a distributed ledger, determining access rights of the data file with respect to another node in the chain of nodes based on the retrieved entitlement mode of the data file, and transmitting information about the event-driven process to the other node based on the determined access rights of the other node.

Dynamic entitlement for blockchain data

A method for applying GPS UAV attitude estimation to accelerate computer vision. The UAV has a plurality of GPS receivers mounted at fixed locations on the UAV. The method includes receiving GPS signals from each GPS satellite in view of the UAV, the GPS measurements comprising pseudo-range and carrier phase data representing the distance between each GPS receiver and each GPS satellite. Carrier phase and pseudo-range measurements are determined for each GPS receiver based on the pseudo-range and carrier phase data. The GPS carrier phase and pseudo-range measurements are compared pair-wise for each pair of GPS receiver and satellite. An attitude of the UAV is determined based on the relative distance measurements. A 3D camera pose rotation matrix is determined based on the attitude of the UAV. Computer vision image search computations are performed for analyzing the image data received from the UAV in real time using the 3D camera pose rotation matrix.

Acceleration of real time computer vision processing on UAVs through GPS attitude estimation

An example operation includes one or more of traversing, by a modeling node, a supply-chain downstream from an initial step, detecting, by a modeling node, a multi-organization step, and responsive to the detection of the multi-organization step, executing a branch prediction algorithm to determine downstream granted organizations.

Computation and prediction of linked access

An example operation may include one or more of releasing, by a subscription service node, at least one blockchain transaction to a plurality of subscribing blockchain nodes requiring direct entitlement access, executing, by the subscription service node, a smart contract to calculate secondary entitlements triggered by the at least one blockchain transaction, determining, by the subscription service node, portions of second entitlement data sets allowed to be accessed by a subset of the plurality of the subscribing blockchain nodes, and sending verification data to the plurality of the subscribing blockchain nodes for verification of the second entitlement data sets.

Subscription service for networks

An example operation includes one or more of detecting a fork in a supply-chain by a modeling node, resolving, by the modeling node, a branch prediction to determine a likely access control, generating, by the modeling node, a range of information based on a branch confidence level, and responsive to the resolution of the branch prediction, revoking access from a document or granting a greater access to the document based on the range.

A method includes defining a two-dimensional geographic region by two-dimensional geographic coordinates to define the bounds of the region, converting each of the two-dimensional coordinates to three dimensional coordinates by way of a lookup stored in a computer readable medium, generating a three-dimensional grid of points, each spaced in an arrangement to encompass coverage of a predetermined ground area, and applying heuristics for a shortest path planning, relative to the three-dimensional grid of points.

Method of optimized path planning for UAVs for the purpose of ground coverage

Systems and methods for capturing images of a target area that include controlling an image sensor mounted to a gimbal to capture images of the target area over time and controlling the gimbal using an elevator algorithm to adjust the orientation of the image sensor.

3D surface estimation and prediction to boost fidelity of realtime LiDAR model generation

The present application relates to systems for programming devices, such as unmanned autonomous vehicles or 'drones,' with a card-based format and methods for using the same. The system and methods generally comprise a programming system that receives one or more instructional cards selected by a user. The system generates an executable program based on the instructional cards received, and transmits the program to a device for execution.

Systems and methods for programing drones

A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, storing heterogeneous data captured by the one or more drones and creating spatio-temporal indices for identifying spatial or temporal coverage gaps in the data necessary to answer the request, controlling the one or more drones to fly over the spatial location to obtain a plurality of data types from the identified spatial or temporal coverage gaps and extracting and analyzing data to answer the request.

System and method to operate a drone

From a hardware sensor at a first time, sensor data comprising a first sample and a second sample is received. The sensor data is modified to create new data, wherein a first data point of the new data corresponds to the first sample, a second data point of the new data corresponds to the second sample, and wherein the first data point and the second data point are arranged differently from the first sample and the second sample. The hardware sensor is emulated by sending, to a consumer of hardware sensor data at a second time, the new data instead of the sensor data.

Sensor data manipulation using emulation

A database comprises historical information of a user's response to previous notifications. The database is accessed to determine a time at which to provide a (new) notification to the user, utilizing at least: a) current user activity status (e.g., determined from measurement information collected from one or more personal devices and/or user calendar events; b) time/day; and c) context information about the notification (e.g., geo-location, indoors/outdoors) including notification type (e.g., calendar entry, email, IM). The user gets the notification via a portable device at the determined time. A machine learning model can select the determined time by discriminating features of the previous notifications for which the user immediately attended versus those that were deferred and/or ignored. Content of the notification can also be altered in view of such discriminating features so as to increase a likelihood the user will immediately attend to the provided notification.

Predicting user attentiveness to electronic notifications

The present application relates to systems for programing devices, such as unmanned autonomous vehicles or 'drones,' with a card-based format and methods for using the same. The system and methods generally comprise a programing system that receives one or more instructional cards selected by a user. The system generates an executable program based on the instructional cards received, and transmits the program to a device for execution.

Systems and methods for programming drones

A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

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Somers, NY, United States of America

Justin Gregory Manweiler