Patents by Inventor Kevin E. Siemonsen
Kevin E. Siemonsen has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11322031Abstract: 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.Type: GrantFiled: December 7, 2015Date of Patent: May 3, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Gregory F. Boland, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin Weisz
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Patent number: 10915118Abstract: 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.Type: GrantFiled: December 23, 2019Date of Patent: February 9, 2021Assignee: International Business Machines CorporationInventors: Gregory F. Boland, James R. Kozloski, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz
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Publication number: 20200150694Abstract: 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.Type: ApplicationFiled: December 23, 2019Publication date: May 14, 2020Inventors: Gregory F. Boland, James R. Kozloski, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz
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Patent number: 10545512Abstract: 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.Type: GrantFiled: April 17, 2019Date of Patent: January 28, 2020Assignee: International Business Machines CorporationInventors: Gregory F. Boland, James R. Kozloski, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz
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Publication number: 20190243390Abstract: 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.Type: ApplicationFiled: April 17, 2019Publication date: August 8, 2019Inventors: Gregory F. Boland, James R. Kozloski, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz
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Patent number: 10345826Abstract: 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.Type: GrantFiled: August 28, 2018Date of Patent: July 9, 2019Assignee: International Business Machines CorporationInventors: Gregory F. Boland, James R. Kozloski, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz
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Publication number: 20190004545Abstract: 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.Type: ApplicationFiled: August 28, 2018Publication date: January 3, 2019Inventors: Gregory F. Boland, James R. Kozloski, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz
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Patent number: 10095243Abstract: 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.Type: GrantFiled: August 9, 2016Date of Patent: October 9, 2018Assignee: International Business Machines CorporationInventors: Gregory F. Boland, James R. Kozloski, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz
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Publication number: 20180114174Abstract: 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.Type: ApplicationFiled: October 24, 2016Publication date: April 26, 2018Inventors: Gregory F. Boland, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz
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Publication number: 20170160752Abstract: 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.Type: ApplicationFiled: August 9, 2016Publication date: June 8, 2017Inventors: Gregory F. Boland, James R. Kozloski, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz
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Publication number: 20170162060Abstract: 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.Type: ApplicationFiled: December 7, 2015Publication date: June 8, 2017Inventors: Gregory F. Boland, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin Weisz
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Patent number: 9471064Abstract: 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.Type: GrantFiled: December 8, 2015Date of Patent: October 18, 2016Assignee: International Business Machines CorporationInventors: Gregory F. Boland, James R. Kozloski, Yu Ma, Justin G. Manweiler, Kevin E. Siemonsen, Umut Topkara, Katherine Vogt, Justin D. Weisz