International Business Machines Corporation

Katherine Vogt

Justin David Weisz

Yu Ma

Gregory F Boland

Justin Gregory Manweiler

Umut Topkara

Kevin E Siemonsen

James R Kozloski

James Thomas Rayfield

Avraham Leff

Russell G Olsen

Kristina Marie Brimijoin

Hani Talal Jamjoom

David M Lubensky

Atul S Kumar

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

An embodiment of the invention provides a method for sending a message from a first user to a second user where an interface receives a message from the second user. A processor connected to the interface identifying one or more keywords in the message and searches an electronic database for the keyword(s) to identify one or more scripted message for the keyword(s). The electronic database includes scripted messages for each keyword in the electronic database. A communications device connected to the processor sends the identified scripted message(s) to the second user.

Cognitive messaging with dynamically changing inputs

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

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

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.

Mechanisms are provided for implementing a personalized health care management system. The mechanisms receive a personalized health care plan for a patient having at least one health goal of the patient, and dynamic patient monitoring data from one or more patient monitoring devices associated with the patient. The mechanisms analyze the dynamic patient monitoring data to determine at least one first communication to output to the patient containing content eliciting conformance of the patient with the personalized health care plan to achieve the at least one health goal. The mechanisms send, to a patient care manager computing device of a patient care manager associated with the patient, a second communication based on results of analyzing the dynamic patient monitoring data. The second communication initiates a new communication session, or continues an existing communication session, between the patient care manager computing device and a patient communication device associated with the patient.

Continuous health care plan coordination between patient and patient care team

A method for controlling a drone includes receiving a natural language request for information about a spatial location, parsing the natural language request into 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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New York, NY, United States of America

Katherine Vogt