Amazon Technologies, Inc.

Broadcom Corporation

Sightic Vista Ltd

Bright Imaging Ltd.

Alst Technical Excellence Center

Noam Sorek

Ilia Vitsnudel

Ianir Ideses

Dotan Kaufman

Eduard Oks

Guy Adam

Ido Yerushalmy

Eli Alshan

Mark Kliger

Ilya Vitsnudel

Liza Potikha

Eran Borenstein

Leo Benedict Baldwin

Larry Alan Pearlstein

Omar Sze Leung

Ilya Vladimirovich Brailovskiy

Amit Singh Chhetri

Ron Fridental

Sharon Alpert

Serkan Hatipoglu

Michael Devyver

David William Devries

Matan Goldman

Imry Kissos

Omer Meir

Lior Fritz

Stewart Robin Shearer

Ridge Carpenter

Brian C Beckman

Amit Kumar Agrawal

Antonio Criminisi

Apoorv Chaudhri

Rohith Mysore Vijaya Kumar

Paul Berenberg

Matthew Alan Townsend

Prakash Ramu

Daniel I Barnea

Nathaniel Bubis

Moshe Bouhnik

Brandon Michael Smith

Paul Alioshin

Abhishek Singh

Ilya Blayvas

Philip James Taylor

Durga Venkata Kiran Yakkala

Hilit Unger

Dmitry Svetlov

Elizaveta Potikha

Yuri Arshavsky

Georgy Melamed

Lamarr Smith

Elizabeth Reisman

Claire Mcgowan

Benjamin Robert Hirsch

Described are systems and methods directed to a body change journey that utilizes one or more current body measurement values, one or more goal body measurement values, and a cohort to determine a body change journey that a user can follow to progress from the current body measurement value to the goal body measurement value. The cohort may include a plurality of data of other bodies who have progressed from a starting body measurement value that is similar to the current body measurement value.

Cohort based body change journeys

Characteristics of a user's movement are evaluated based on performance of activities by a user within a field of view of a camera. Video data representing performance of a series of movements by the user is acquired by the camera. Pose data is determined based on the video data, the pose data representing positions of the user's body while performing the movements. The pose data is compared to a set of existing videos that correspond to known errors to identify errors performed by the user. The errors may be used to generate scores for various characteristics of the user's movement. Based on the errors, exercises or other activities to improve the movement of the user may be determined and included in an output presented to the user.

System for correcting user movement

Video output is synchronized to the actions of a user by determining positions of the user's body based on acquired video of the user. The positions of the user's body are compared to the positions of a body shown in the video output to determine corresponding positions in the video output. The video output may then be synchronized so that the subsequent output that is shown corresponds to the subsequent position attempted by the user. The rate of movement of the user may be used to determine output characteristics for the video to cause the body shown in the video output to appear to move at a similar rate to that of the user. If the user moves at a rate less than a threshold or performs an activity erroneously, the video output may be slowed or portions of the video output may be repeated.

System for synchronizing video output based on user activity

First video data representing performance of an activity by a first user is acquired. Poses of the first user are determined from the first video data. Second video data is generated based on the determined poses and based on appearance data that represents a second user, such as a model, paid performer, and so forth, in various poses. The resulting second video data depicts the second user performing the same poses as the first user. The second video data may then be sent to a recipient. For example, a participant in an exercise class may send a video to an instructor that depicts what appears to be the paid performer performing the poses, instead of the participant. As a result, video data showing the participant is not shared, protecting the privacy of the participant while still allowing them to participate and interact.

System for generating privacy-preserving video data

An image is processed to determine semantic data about what is depicted in that image. The semantic data provides information about location in the image and semantic category of an object depicted within the image. The semantic categories may include 'background', “face”, “text”, “clothing”, “exercise”, and so forth. The semantic data may then be used to facilitate distribution of image data while limiting the information shared. For example, the semantic data may indicate the placement and position of a user exercising in first video data. Using the semantic data, second video data may be produced that obscures or replaces parts of the image that do not depict the user exercising. The second video data may then be used for providing feedback to the user, while preventing disclosure of other people, background, and so forth that are depicted in the first video data.

System for access control of image data using semantic data

Video output is generated based on first video data that depicts the user performing an activity. Poses of the user during performance of the activity are compared with second video data that depicts an instructor performing the activity. Corresponding poses of the user's body and the instructor's body may be determined through comparison of the first and second video data. The video data is used to determine the rate of motion of the user and to generate video output in which a visual representation of the instructor moves at a rate similar to the that of the user. For example, video output generated based on an instructional fitness video may be synchronized so that movement of the presented instructor matches the rate of movement of the user performing an exercise, improving user comprehension and performance.

System for synchronizing video output based on detected behavior

Systems for assisting a user in performance of a meditation activity or another type of activity are described. The systems receive user input and sensor data indicating physiological values associated with the user. These values are used to determine a recommended type of activity and a length of time for the activity. While the user performs the activity, sensors are used to measure physiological values, and an output that is provided to the user is selected based on the measured physiological values. The output may be selected to assist the user in reaching target physiological values, such as a slower respiration rate. After completion of the activity, additional physiological values are used to determine the effectiveness of the activity and the output that was provided. The effectiveness of the activity and the output may be used to determine future recommendations and future output.

Interface for guided meditation based on user interactions

A first user generates video data for performance of an activity, such as a fitness exercise, by performing the activity in front of a camera. Based on the video data, the amount of movement of different parts of the first user's body is determined. Data representing the position of the first user over time is generated. The data may take the form of a function or a signal that is based on the function. The locations of body parts that move significantly are prioritized over other body parts when determining this data. At a subsequent time, a second user performs the activity. The number of times the second user completes the activity is counted by determining the number of times the second user reaches a position corresponding to a maximum value in the data representing the position of the first user.

System for learning and tracking performance of activities

Information about changes in vision over time is useful to determine the health of a user. An electronic device has a display that presents visual information to a user. A sensor generates distance data that is used to determine a distance between the user and the display at particular times. Other data may also be acquired that is indicative of whether the user is wearing glasses, tilt of the user's head relative to the display, ambient light level, display brightness, what is being presented on the display such as video content or text content, font size, and so forth. The data is used to determine if the user's vision has changed beyond a threshold amount. If so, an action may be taken, such as providing a recommendation to the user. For example, the user may be advised to consult a health care provider.

System for monitoring vision changes

A computer-implemented method includes receiving first pose data for a first human represented in a first image, receiving second pose data for a second human represented in a second image, receiving first semantic segmentation data for the first image, and receiving second semantic segmentation data for the second image. A pose-aligned second image can be generated by modifying the second image based on the first pose data, the second pose data, the first semantic segmentation data, and the second semantic segmentation data. A mixed image can be determined by combining pixel values from the first image and pixel values of the pose-aligned second image according to mask data. In some embodiments, the mixed image includes a representation of an outfit that includes first clothing represented in the first image and second clothing represented in the second image.

Method and system for generating combined images utilizing image processing of multiple images

A decision engine executing on an electronic device may determine, using sensor data captured by multiple sensors of the device, whether a user is present in an environment that includes the device. If the user is determined to be present in the environment, the device may transition from a first state to a second state. The first state may be a first power state of the device in which the device is powered off or an idle or dormant state in which the device is powered on but a display of the device is powered off. Correspondingly, the second state may be a second power state of the device in which the device and the display are powered on and content is being rendered on the display. If the decision engine cannot make a determination based on the sensor data, a context engine may adjudicate the user presence determination.

Determining a device state based on user presence detection

Devices, systems and methods are disclosed for generating stitched video data by combining video data from multiple image capture devices. For example, video data from a first image capture device and a second image capture device located near the first image capture device may be combined to generate unique panoramas. The system may determine transitions between the video data based on a focus distance to improve the stitched video data. The system may remove individual objects from first video data by substituting corresponding portions of second image data. The system may determine depth information associated with a first object represented in the video data and may add additional objects in front of or behind the first object, properly scaling and masking the additional object. The system may generate a virtual environment by weighting images from individual image capture devices and the virtual environment may be three dimensional.

Multiple camera panoramic images

This disclosure describes an unmanned aerial vehicle ('UAV') including an inflatable membrane (e.g., a balloon) and a compressed gas chamber containing a gas (e.g., helium, hydrogen, etc.) for inflating the membrane. When the UAV is approaching or departing from a location where noise reduction is desirable (e.g., a delivery location), the membrane may be inflated so as to increase the buoyancy of the UAV and allow the propulsion system (e.g., utilizing propellers, etc.) to be operated with less thrust and correspondingly with less noise. Once the UAV has departed and reached a certain distance from the location, the membrane may be deflated and retracted back into a storage area of the UAV.

Unmanned aerial vehicle with inflatable membrane

Devices and techniques are generally described for image editing. In various examples, color image data and segmentation image data may be identified. In some examples, the segmentation image data may identify a first portion of the color image data as a foreground region and a second portion of the color image data as a background region. A segmentation boundary may be identified between the foreground region and the background region. A first area of the second portion may be identified, where the first area extends from the segmentation boundary to a first area outer boundary. A second area of the second portion may be identified, where the second area extends from the first area outer boundary to a second area outer boundary. A blended and blurred representation of the first area and the second area may be generated.

Generation of semantic depth of field effect

Various examples are directed to systems and methods for formatting stitched panoramic frames for transmission to a remote image processing system, the method comprising. A control circuit of a panoramic camera may receive first and second frames captured by first and second image sensors having partially overlapping fields-of-view. The control circuit may identify a first overlap region of the first frame and a second overlap region of the second frame. The first and second overlap region may comprise pixel values from portions of the fields-of-view of the image sensors that overlap. The control circuit may generate a panoramic frame from based at least in part on the first and second frames. The control circuit may generate a transmission frame comprising the panoramic frame, the first overlap region and the second overlap region.

Formatting stitched panoramic frames for transmission

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Zichron Yaacov, Israel

Noam Sorek