Microsoft Technology Licensing, LLC

Motorola Corporation

Jyh-Han Lin

Stephen P DiAcetis

Pradipta Kumar Basu

Chih-Wei Wang

Gang Zhao

Aravind Seshadri

Arjun Sundararajan

Sindhura Bandhakavi

Sharad Agarwal

Moe Khosravy

Miller Thomas Abel

Timothy David Sharpe

Shai Herzog

James Kai Yu Lau

Katrika Marie Morris

Katrika Marie Woodcock

Weili Liu

Chester Overstead Murphy

Jin Li

John Charles Krumm

Rui Liang

Lon-Chan Chu

Gursharan Singh Sidhu

Christopher R Rice

Vivek Gupta

Patrick E Plaisted

Ronald R Smith

Ankit Srivastava

Anand Muthurajan

Robert L Geiger

Sandeep Patnaik

Fernando Gonzalez

Cristina Del Amo Casado

Anoop Anantha

Ke-Cheng Huang

Julia Maureen Letchner

Hiep V Khuu

Sanjay Wanchoo

Jenna Hong

David Mahlon Hoover

Jonathan Matthew Kay

Priyanka Bhargavi Vegesna

Ehimwenma Nosakhare

Alex C Wang

Haiyang Wang

Mawulolo Koku Ameko

Prasanta Ghosal

Joel Robert Bergman

Vanessa Tieh-Su Wu

Kabir Walia

Sean Gormley T Kelley

Ashish Neupane

Naga Venkata Rajagopal Ramadugu

Nandita Sharma

Fan Yin

Lingxiao Hang

XiWen Yue

Hu Lu

Fadi Al Sayed Hassan

Pavani Haridasyam

Ryan Bane

Yong Song

Steve DiAcetis

Francis X Dougherty

Anup Kashinath Pachlag

Alan W Chan

Patrick Plaisted

Stephen P Diacetis

Aravind K Seshadri

Steve Diacetis

A computer-implemented method for proactively detecting and remediating anomalous devices includes accessing, via a network, device attributes corresponding to enterprise devices within an enterprise network, providing the device attributes to a supervised machine learning model, and predicting, via the supervised machine learning model, whether each enterprise device is healthy or anomalous, where the enterprise device is predicted to be healthy unless the supervised machine learning model determines that the probability of the enterprise device being anomalous exceeds a specified confidence threshold. The method includes, for each enterprise device that is predicted to be anomalous, perturbing a portion of the corresponding device attributes via an automated counterfactual generator to generate synthetic data representative of counterfactual healthy devices. The method includes generating recommended remedial action(s) that will cause each enterprise device to approximate each counterfactual healthy device and causing surfacing, via a user interface, of the recommended remedial action(s).

Proactively detecting and remediating anomalous devices using supervised machine learning model and automated counterfactual generator

The present disclosure relates to systems, methods, and computer-readable media for receiving usage data for a virtual device (or other virtual service), analyzing the usage data to determine a usage bucket characteristic of usage of the virtual device over a period of time, and determining a usage score for the virtual device. The systems described herein further involve causing a deployment of the virtual device to be upgraded, downgraded, or otherwise modified based on the usage bucket and associated usage score. The features and functionalities described herein can provide an efficient mechanism for administrating a tenant deployment as well as implementing a more efficient utilization of cloud computing resources for a variety of virtual services.

Upgrading a virtual device deployment based on spike utilization

A system and method determining an error radius reflecting the accuracy of a calculated position of a processing device is provided. A data structure includes an error radius mapped to a scaled geographic area or 'tile' comprising an area in which a calculated position may be determined. The data structure may include a plurality of first fields identifying a scaled geographic area based on a global projection reference system, and a plurality of second fields identifying, for each of the first fields, a position error radius associated with a scaled geographic area and level. For any calculation of an inferred position based on beacon observations, a rapid lookup of the corresponding scaled area including the new inferred position in the data structure returns an error radius for the new inferred position.

Location error radius determination

A method and apparatus is described for rendering web pages by a wireless device in order to reduce an amount of data transmitted over-the-air to the wireless device. In one embodiment, a method is performed by a server, comprising receiving a markup language document from a web server via a network interface, the markup language document corresponding to a web page. Next, the markup language document is parsed into a number of blocks by a processor, each of the blocks comprising code representing original content of the web page. Then, the processor creates one or more replacement blocks, each of the replacement blocks corresponding to a respective one of the blocks, each of the replacement blocks comprising code representing substituted content of the web page. Some of the blocks and at least one of the replacement blocks are then sent to a wireless device for rendering.

Web page rendering on wireless devices

A system and method for calculating a position in response to a position request. Observed beacon data associated with the request is used to select a calculation method based on available data for a venue and device capabilities. If sufficient venue data based on previously verified beacon positions is available, a position calculation can resolve floor and venue information. If insufficient previously observed data is available for a venue, the position is calculated using 2D data based on GPS observations. Following the choice a calculation model, the calculation position is returned in response to the position request.

Adaptive position determination

The description relates to receiver gain offset. One example can obtain data sensed by a mobile device at a position. The example can evaluate the sensed data and survey data to identify a venue proximate to the position. The survey data of the venue can be organized into regions and then individual regions can be organized into sub-regions. The example can compare signal strengths of the sensed data to signal strengths of the survey data to identify the position relative to an individual region. The comparison can utilize a receiver gain offset estimation between the mobile device and another device that acquired the survey data. The example can refine the receiver gain offset estimation and attempt to identify the position relative to an individual sub-region within the individual region utilizing the refined receiver gain offset estimation.

Receiver gain offset

A determination of whether a mobile computing device is indoors or outdoors can incorporate any of a variety of factors to make an efficient and accurate determination of indoor-outdoor status. Such a status can be useful for use in conjunction with positioning services. Features such as bounding boxes, activity determination, and the like can be used to strike a balance between power consumption and accuracy. A positive user experience with fewer false detections can result.

Accounting for indoor-outdoor transitions during position determination

Embodiments adjust device error radiuses associated with inferred device positions produced by positioning systems. Inferred beacon positions and associated beacon radiuses are accessed for beacons in a beacon fingerprint from an observing computing device. The beacon radiuses are associated with a pre-defined confidence level (e.g., an in-circle percentage). A Kalman filter is applied to at least one of the beacons using the inferred beacon positions and the beacon radiuses associated therewith to infer a device position for the computing device and to compute a device error radius for the inferred device position. The computed device error radius is adjusted as a function of the quantity of beacons input to the Kalman filter to achieve the pre-defined confidence level.

Correcting device error radius estimates in positioning systems

Defining subscriptions to location information for a computing device (e.g., a mobile computing device). Application programs, services, and/or peer devices define the subscriptions based on constraints associated with requests for the location information. A location request scheduler coordinates the subscriptions temporally and spatially to reduce the quantity of the requests from the computing device. In some embodiments, the subscriptions are automatically defined based on an observed mobility pattern of the computing device.

Constraint-based scheduling for delivery of location information

Embodiments infer position information using captured content items. A computing device such as a mobile computing or a cloud service analyzes one or more content items to generate information describing the content items. For content items that include photographs, for example, the photographs are processed to identify faces, objects, places, text, etc. The generated information is compared to reference content items (e.g., image comparison), user data (e.g., calendar data, contact information, etc.), and/or public data (e.g., address information, event information, etc.) to identify at least one match. Location information associated with the matched reference content items, user data, and/or public data is obtained. A position is determined for the content items based on the location information obtained from the matched reference content item, user data, and/or public data. In some embodiments, the obtained location information is used to refine a position determined using a beacon-based positioning service.

Inferring positions with content item matching

Managing use of a location sensor on a computing device for energy efficiency. The location sensor is briefly initialized to measure the signal quality. The measured signal quality is compared to pre-defined signal criteria values. The signal criteria values correspond to acceptable energy consumption, for example. If the signal criteria values are satisfied, location information for the computing device is obtained. Otherwise, the location sensor is disabled without obtaining the location information. In some embodiments, a lower-energy location sensor is used to obtain location information to determine whether to enable a higher-energy location sensor based on expected energy consumption.

Adaptive energy-efficient location determination

Embodiments calculate an estimated latency between computing devices. A latency service aggregates latency records defining latency measurements and corresponding latency factors from a plurality of computing devices. From the aggregated latency records, the latency service defines relationships between the latency measurements and the corresponding latency factors. Responsive to a request for an estimated latency from a mobile computing device, the latency service applies the defined relationships to estimate the latency based on the latency factors associated with the received request. In some embodiments, the estimated latency includes three portions: a first latency value representing the latency from the mobile computing device to a cell site, a second latency value representing the latency from the cell site to an access point, and a third latency value representing the latency from the access point to a destination computing device.

Network latency estimation for mobile devices

Determining geospatial patterns from device data collected from a plurality of computing devices. The devices represent, for example, a plurality of sources providing the device data. The device data describes the computing devices and/or environments thereof. Some embodiments present the determined patterns to users for editing, update maps with the edited patterns, and distribute the maps to the users. The maps are stored to create a searchable map library.

Identifying geospatial patterns from device data

Growing community of inventors

Mercer Island, WA, United States of America

Jyh-Han Lin