Kevin E Siemonsen

20190243390, Aug 8, 2019

Apr 17, 2019

16/386465

- Armonk NY, US
James R. Kozloski - New Fairfield CT, US
Yu Ma - White Plains NY, US
Justin G. Manweiler - Somers NY, US
Kevin E. Siemonsen - New Fairfield CT, US
Umut Topkara - White Plains NY, US
Katherine Vogt - New York NY, US
Justin D. Weisz - Stamford CT, US

G05D 1/10
G06N 20/00
G05D 1/00
G06N 7/00
G06F 17/27
B64C 39/02
G08G 5/00

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.

20190004545, Jan 3, 2019

Aug 28, 2018

16/114466

- Armonk NY, US
James R. Kozloski - New Fairfield CT, US
Yu Ma - White Plains NY, US
Justin G. Manweiler - Somers NY, US
Kevin E. Siemonsen - New Fairfield CT, US
Umut Topkara - White Plains NY, US
Katherine Vogt - New York NY, US
Justin D. Weisz - Stamford CT, US

G05D 1/10
G06N 99/00
G06N 7/00
G06F 17/27
G05D 1/00
G08G 5/00
B64C 39/02

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.

20180114174, Apr 26, 2018

Oct 24, 2016

15/332462

- Armonk NY, US
Yu Ma - White Plains NY, US
Justin G. Manweiler - Somers NY, US
Kevin E. Siemonsen - New Fairfield CT, US
Umut Topkara - Scarsdale NY, US
Katherine Vogt - New York NY, US
Justin D. Weisz - Stamford CT, US

G06Q 10/06
B64C 39/02

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. The marketplace matches the drone service requests with drones registered in the marketplace capable of performing the requested service. The drone operator and the drone service requester form an agreement for a job to be performed.

20170160752, Jun 8, 2017

Aug 9, 2016

15/232366

- Armonk NY, US
James R. Kozloski - New Fairfield CT, US
Yu Ma - White Plains NY, US
Justin G. Manweiler - Somers NY, US
Kevin E. Siemonsen - New Fairfield CT, US
Umut Topkara - White Plains NY, US
Katherine Vogt - New York NY, US
Justin D. Weisz - Stamford CT, US

G05D 1/10
G06F 17/27
G06N 99/00
G06N 7/00
G08G 5/00
G05D 1/00

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.

20170162060, Jun 8, 2017

Dec 7, 2015

14/961610

- Armonk NY, US
Yu Ma - White Plains NY, US
Justin G. Manweiler - Somers NY, US
Kevin E. Siemonsen - New Fairfield CT, US
Umut Topkara - Scarsdale NY, US
Katherine Vogt - New York NY, US
Justin Weisz - Stamford CT, US

G08G 5/00
B64D 47/08
G05D 1/10
G06T 17/05
B64C 39/02

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.