Caption Health, Inc.

X Development LLC

Google Inc.

Loon LLC

Bay Labs, Inc.

Johan Mathe

Michael G Cannon

Charles Cadieu

Kilian Koepsell

Nicolas Poilvert

Ha Hong

Keith Allen Bonawitz

Nathanael Romano

Natalia Bilenko

Charles Chen

Nina Miolane

C O Lee Boyce, Jr

Martin Wojtczyk

A method for clip selection includes receiving through an interface to a medical imaging device, a selection of a diagnostic procedure and a target portion of a mammalian body. Thereafter, the device acquires video clip imagery of the target portion and stores the video clip imagery in an image store. Each video clip of the video clip imagery is then image processed to determine a view and a quality of each video clip and a rule is retrieved from a rules base corresponding to the selected diagnostic procedure and target portion. In this regard, the rule specifies a requisite view and quality of the video clip imagery. Finally, the retrieved rule is applied to the video clip imagery as a filter to produce a subset of video clip imagery satisfying the specified requisite view and quality and the subset of video clip imagery is stored in the image store.

Video clip selector for medical imaging and diagnosis

Example implementations may relate to selection between a first mode and a second mode. The first mode may involve (i) directing an aerial vehicle (e.g., in an aerial network including a plurality of aerial vehicles) to navigate to each of a plurality of altitudes and (ii) determining respective wind-related data at each respective altitude. Whereas, the second mode may involve (i) selecting at least one altitude based on the determined wind-related data and (ii) directing the aerial vehicle to reposition to the at least one selected altitude. As such, a control system may determine flight data for the aerial vehicle. Based on the flight data, the control system may make a selection between the first mode and the second mode. And based on the selection, the control system may then operate the aerial vehicle according to the first mode or may operate the aerial vehicle according to the second mode.

Selection between explore mode and control mode for aerial vehicle

Embodiments of the invention provide a method, system and computer program product for artificially intelligent ejection fraction determination. In a method for artificially intelligent ejection fraction determination, a neural network is loaded into memory of a computer, that has been trained with different sets of cardiac imaging data acquired during imaging of a ventricle for different hearts and a known ejection fraction for each of the sets. Then, a contemporaneous set of imaging data is acquired of a ventricle of a heart and the contemporaneous set of imaging data is provided to the neural network. Finally, an ejection fraction determination output by the neural network is displayed in a display of the computer without tracing a ventricle boundary of the heart.

Artificially intelligent ejection fraction determination

An example computer-implemented method involves determining that an aerostat is located within a first atmospheric layer. A first wind-velocity measure is indicative of wind velocity in the first atmospheric layer. The method also involves determining an altitude of an atmospheric layer boundary between the first atmospheric layer and a second atmospheric layer. A second wind-velocity measure is indicative of wind velocity in the second atmospheric layer, and the second wind-velocity measure differs from the first wind-velocity measure by at least a threshold amount. The method further involves determining a desired location for the aerostat, and during at least a portion of a flight towards the desired location, causing the aerostat to traverse back and forth across the atmospheric layer boundary while remaining within a predetermined vertical distance from the atmospheric layer boundary in order to achieve a desired horizontal trajectory.

Efficient aerostat navigation by moving between atmospheric layers

This disclosure relates to the use of an optimal altitude controller for super pressure aerostatic balloon in connection with a balloon network. The aerostatic balloon includes a bladder containing a gas that is lighter than the air present in the environment of the balloon. Additionally, the aerostatic balloon includes an envelope filled with air. A mass-changing unit configured to selectively add or remove air may control the amount of air in the envelope. Further, the balloon has a communication module configured to transmit data relating to a current balloon state, and receives data relating to a desired balloon state. Additionally, the balloon includes a processor configured to control the mass-changing unit based on the desired balloon state. The mass-changing unit of the aerostatic balloon may be powered by a renewable energy source, such as solar power. The mass-changing unit adds or removes air with an impeller.

Optimal altitude controller for super pressure aerostatic balloon

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San Francisco, CA, United States of America

Johan Mathe