Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Mechanisms are provided to implement a personalized health care management system. The mechanisms receive a personalized health care plan for a 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 identify at least one pattern of dynamic patient monitoring data representing a habit of the patient, and generate desired pattern data based on results of the analysis. The desired pattern data represents at least one desired habit for the patient. The mechanisms determine at least one deviation of the desired pattern data from the at least one pattern of dynamic patient monitoring data, and perform at least one health management operation to assist the patient in minimizing the determined at least one deviation.

Abstract: Mechanisms are provided for implementing a personalized health care management system. The mechanisms receive a personalized health care plan for a patient, and dynamic patient monitoring data from patient monitoring devices associated with the patient. The mechanisms analyze the dynamic patient monitoring data to identify at least one pattern of dynamic patient monitoring data representing a habit of the patient. The mechanisms generate desired pattern data based on results of the analysis. The desired pattern data represents at least one desired habit for the patient. The mechanisms determine at least one communication to output to the patient via a patient computing device or patient communication device to elicit conformance of the patient with the at least one desired habit based on the generated desired pattern data and the personalized health care plan. The mechanisms output the at least one communication to the patient computing device or patient communication device.

Abstract: 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.

Abstract: Aspects of the invention relate to a UAV (drone) data marketplace where requests for UAV services are matched with registered UAVs. Drone operators register in the marketplace drones in their fleet with their capabilities and requesters of drone services make their request in the marketplace. The requests can be a set of data to be collected (e.g., optical images, NIR data, temperatures, etc.) and/or actions to be performed (e.g., deploying spare machinery parts to a tractor in the field), a location from which that data is collected and/or location to which a delivery is to be made, a pipeline of analytics to be performed on the data (e.g., optical recognition, NDVI computation, fertilizer application recommendation), a timeframe in which to collect the data (e.g., “by next week”, “by the end of today”), and a market value for how much the Requester is willing to pay for that data or operation.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.