Microsoft Technology Licensing, LLC

John Benjamin Hesketh

Kenneth Liam Kiemele

Jeffrey Sipko

Christian Michael Sadak

Bryant Daniel Hawthorne

Adolfo Hernandez Santisteban

Donna Katherine Long

Mario Emmanuel Maltezos

Aaron Burns

Kendall Clark York

Roger Sebastian Sylvan

Charlene Mary Jeune

Evan Lewis Jones

LaSean Tee Smith

Christian Klein

Arthur Charles Tomlin

Hubert Van Hoof

Gregg Robert Wygonik

Michael Robert Thomas

Andrew Austin Jackson

Jamie R Cabaccang

Timothy David Kviz

Sophors Khut

Ricardo Arturo Espinoza Reyes

Charlene Mary Atlas

Priya Ganadas

Jennifer Jean Choi

James Lewis Nance

Nir Finkelstein

Jeffrey R Sipko

Techniques for generating a machine learning model to detect event instances from physical sensor data, including applying a first machine learning model to first sensor data from a first physical sensor at a location to detect an event instance, determining that a performance metric for use of the first machine learning model is not within an expected parameter, obtaining second sensor data from a second physical sensor during a period of time at the same location as the first physical sensor, obtaining third sensor data from the first physical sensor during the period of time, generating location-specific training data by selecting portions of the third sensor data based on training event instances detected using the second sensor data, training a second ML model using the location-specific training data, and applying the second ML model instead of the first ML model for detecting event instances.

Automated localized machine learning training

A computer system is provided that includes a server configured to store a plurality of location accounts, each location account being associated with a physical space at a recorded geospatial location. The plurality of location accounts utilize shared data definitions of a physical space parameter. Each physical space is equipped with a corresponding on-premise sensor configured to detect measured values for the physical space parameter over time and send to the server a data stream indicating the measured values. The computer system further includes a network portal, via which an authorized user for a location account can selectively choose whether to share the measured values or a summary thereof with other location accounts via the network portal.

Sharing measured values of physical space parameters

A computing system is provided, including a first display device having a first hardware configuration including a first display and a second display device having a second hardware configuration different from the first hardware configuration and including a second display. The computing system may further include a processor configured to receive an input including instructions to launch an application program on the first display device. The application program may include application program hardware specifications indicating hardware used by the application program. Based on the first hardware configuration, the second hardware configuration, and the application program hardware specifications, the processor may be further configured to determine that the second hardware configuration matches the application program hardware specifications more closely than the first hardware configuration. The processor may be further configured to launch the application program on the second display device.

Display device selection based on hardware configuration

A device for removal of personally identifiable data receives monitoring data acquired by a sensor. The monitoring data including personally identifiable data relating to one or more individuals being monitored. The system processes the acquired monitoring data to remove the personally identifiable data by at least one of abstraction or redaction while the monitoring data is located on the device. The processed monitoring data having the personally identifiable data removed can thereby be transmitted external to the device with reduced security risk.

Removing personally identifiable data before transmission from a device

A wearable device is configured with a one-dimensional depth sensor (e.g., a LIDAR system) that scans a physical environment, in which the wearable device and depth sensor generate a point cloud structure using scanned points of the physical environment to develop blueprints for a negative space of the environment. The negative space includes permanent structures (e.g., walls and floors), in which the blueprints distinguish permanent structures from temporary objects. The depth sensor is affixed in a static position on the wearable device and passively scans a room according to the gaze direction of the user. Over a period of days, weeks, months, or years the blueprint continues to supplement the point cloud structure and update points therein. Thus, as the user continues to navigate the physical environment, over time, the point cloud data structure develops an accurate blueprint of the environment.

Using a one-dimensional ray sensor to map an environment

The present disclosure provides apparatus and methods for automated tracking and counting of objects in a set of image frames using a resource-constrained device based on analysis of a selected subset of image frames, and based on selectively timing when resource-intensive operations are performed.

Apparatus and methods of automated tracking and counting of objects on a resource-constrained device

The herein described technology facilitates sharing of notes and files with a locked computing device. The locked computing device may receive a file sharing request that includes a file identifier identifying a location of a source file. The locked processing device provides a user account of the processing device with access to content of the source file responsive to authentication of a recipient access credential associated with the user account.

Note and file sharing with a locked device

A computer system is provided that includes a camera device configured to capture images of a physical scene and a processor configured to store a list of identified blobs. Each identified blob includes a determined blob feature and a path of tracked positions for that identified blob. The processor is configured to receive a series of images of the physical scene from the camera device, detect a candidate blob in a candidate image of the series of images, determine a blob feature of the candidate blob and a position of the candidate blob in the candidate image captured by the camera device. For each identified blob in the list, the processor is configured to compute a match score between the candidate blob and that identified blob.

Blob detection using feature match scores

Techniques are described herein that are capable of making a state-based determination of information to be displayed on a screen region of a hinge that is coupled to other screen regions. For instance, a state of a hinge that is coupled between a first screen region and a second screen region may be determined. Information that is to be displayed on a third screen region that is provided on the hinge may be determine based at least in part on the state of the hinge.

Device having a screen region on a hinge coupled between other screen regions

A wearable device is configured with various sensory devices that recurrently monitor and gather data for a physical environment surrounding a user to help locate and track real-world objects. The various heterogeneous sensory devices digitize objects and the physical world. Each sensory device is configured with a threshold data change, in which, when the data picked up by one or more sensory devices surpasses the threshold, a query is performed on each sensor graph or sensory device. The queried sensor graph data is stored within a node in a spatial graph, in which nodes are connected to each other using edges to create spatial relationships between objects and spaces. Objects can be uploaded into an object graph associated with the spatial graph, in which the objects are digitized with each of the available sensors. This digital information can be subsequently used to, for example, locate the object.

Object and location tracking with a graph-of-graphs

To address issues of capturing and processing images, a mobile computing device is provided. The mobile computing device may include a two-part housing coupled by a hinge, with first and second parts that include first and second displays, respectively. The hinge may permit the displays to rotate throughout a plurality of angular orientations. The mobile computing device may include one or more sensor devices, processor, first camera, and second camera mounted in the housing. The one or more sensor devices may be configured to measure the relative angular displacement of the housing, and the processor may be configured to process images captured by the first and second cameras according to a selected function based upon the relative angular displacement measured by the one or more sensor devices.

Image capture using a hinged device with multiple cameras

A hinged multi-screen computing device is provided that includes a housing having first and second parts coupled by a hinge, each part including a display, a sensor mounted in the housing and configured to detect a hinge angle between the first and second parts, and processor configured to render and display a three-dimensional representation of a virtual object on the first display, the three-dimensional view of the virtual object being rendered based upon the detected hinge angle.

Manipulating virtual objects on hinged multi-screen device

A mobile computing device is provided that includes a processor, an accelerometer, and a housing with two parts, the first part including a first camera and a first display device, and the second part including a second camera and a second display device. When a predetermined trigger condition is satisfied, the processor is configured to cause the first and second display devices to each simultaneously display image data captured by the second camera, and image data received from a remote computing device.

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Kirkland, WA, United States of America

John Benjamin Hesketh