Microsoft Technology Licensing, LLC

Microsoft Corporation

Venkata N Padmanabhan

Ramachandran Ramjee

Lili Qiu

Vishnu Navda

Philip Andrew Chou

Jitendra Dattatraya Padhye

Sharad Agarwal

Paramvir Bahl

Saikat Guha

Kamal Jain

Daniel Ron Simon

Ranjita Bhagwan

Rajatish Mukherjee

Mudit Jain

Luis Felipe Cabrera

Ranveer Chandra

Yi-Min Wang

Jiahe Helen Wang

Prashanth Hosabettu Mohan

Lenin Ravindranath Sivalingam

Krishna Chintalapudi

Ashish Sharma

Helen Jiahe Wang

Shravan Rayanchu

Wilfred G Russell

Qixiang Sun

Devarayasamudra Chandramouli Vageesh

Piyush Agrawal

Lakshminarayanan Subramanian

Albert Gordon Greenberg

Ming Zhang

Pradeep Padala

Yunnan Wu

Ganesh Ananthanarayanan

Gursharan Singh Sidhu

Behrooz Chitsaz

Manuel Silverio Da Silva Costa

Parantap Lahiri

Mohamed Jawad Khaki

Lorenzo De Carli

Anand Padmanabha Iyer

Anshul Rai

Bhavish Aggarwal

Abhishek Kumar Sharma

Geoffrey Michael Voelker

Tathagata Das

Rajdeep Das

Akshay Uttama Nambi Srirangam Narashiman

Ishit Mehta

Rijurekha Sen

Shruthi Bannur

Sanchit Gupta

Ankush Jain

Najam Ahmad

Aaron Schulman

Joseph Dilip Antony

Monojit Choudhury

Ravindran Kannan

Nikitas Liogkas

Nimantha Thushan Baranasuriya

Manveer Singh Chawla

Junchen Jiang

Prashanth Mohan

A platform that facilities preservation of user privacy with respect to location-based applications executing on mobile computing devices is described. The platform registers triggers that are set forth by location-based applications, where a trigger specifies one or more rules and includes a location constraint. The platform causes a sensor on the mobile computing device to output location data, and the platform determines if the trigger has been satisfied by comparing the location constraint with the location data. If the trigger is satisfied, the platform transmits a callback to the application. Accordingly, the application does not receive location data from the sensor.

Privacy preservation platform

A 'Data Transfer Tool' extracts, labels and stores user data or information that may be confined within application silos during user interaction with arbitrary apps. The Data Transfer Tool enables sharing of this otherwise siloed data shared across and between authorized apps. The Data Transfer Tool provides a task-centric approach to define and extract structured and semantically meaningful information from source applications by providing multiple semantic models that are individually tailored to particular source apps. The Data Transfer Tool applies an accessibility API or the like of the OS in combination with the semantic model for the source app to scrape user entered or selected data or information from the source app. The Data Transfer Tool enables and creates new user experiences and increases user efficiency when interacting with various apps by making the scraped data or information available to subscribing destination apps approved to receive that data or information.

Automated data transfer from mobile application silos to authorized third-party applications

Systems and techniques for monitoring driver state are described herein. In an example, a driver state monitoring system is adapted to receive a set of color images of a person, such as images of a driver of a vehicle with varying levels of illumination in the images. The driver state monitoring system may be further adapted to generate a set of synthesized thermal images from the set of color images. The driver state monitoring system may be further adapted to use a trained thermal image face detector to locate a human face in the synthesized thermal images. The driver state monitoring system may be further adapted to use a trained thermal image facial landmark predictor to locate facial landmarks in the synthesized thermal images. The driver state monitoring system may be further adapted to analyze the facial landmarks in the synthesized thermal images to determine facial feature movements.

Systems and methods for monitoring driver state

A process of determining queueing delay. The process sends a high priority ping request to an access point; sends a low priority ping request to the access point; receives a high priority ping response from the access point; receives a low priority ping response from the access point; and calculates the queuing delay based on the difference in the round trip times of the low priority ping request from the high priority ping request.

Ping pair technique for detecting wireless congestion

Techniques for determining network paths for voice calls include analyzing network path measurements of the network paths to determine historical network performance data for the network paths, and identifying a group of top-k network paths based on those network paths having better network performance data other network paths. A particular network path may be selected using various techniques, such as selecting the network path with the best historical network performance, selecting a network path by applying a multi-armed bandit algorithm to select the path from the group of top-k network paths, or selecting a network path at random. The selected network path may be used to by a source-destination pair of computing devices for a voice call, and a record of that voice call may be used to update network performance information for the particular network path.

Data-driven network path selection

A method for managing data communication of a mobile device in a mobile network is provided. The method may include receiving a request, at a cloud proxy, to retrieve Internet data from the Internet. The request may originate from an application, and the Internet data may have a plurality unique objects. The method may also include aggregating the Internet data and two or more of the unique objects from the Internet. Furthermore, the method may include forwarding the Internet data and the two or more unique objects to the mobile device in one transmission.

Cellular data communication for mobile devices

A method for locating a user in a wireless network is disclosed. A mobile computer seeking to determine its location within a building detects the signal strength of one or more wireless base stations placed at known locations throughout the building. The mobile computer uses this measured signal strength to determine its location via a signal-strength-to-location table look-up. A table of known locations within the building and the base station signal strength at those locations is searched to find the most similar stored signal strength to the signal strength detected. The location corresponding to the most similar stored signal strength is determined to be the current location of the mobile computer. Alternatively, a number of signal strengths from the table can be used and the corresponding locations can be spatially averaged to determine the location of the mobile computer. The table can be derived empirically, by placing a mobile computer at the known locations and detecting the signal strength of the wireless base stations at those locations, or the table can be derived mathematically by taking into account a reference signal strength, the distance between the reference point and the known location, and the number of walls between the reference point and the known location. As an alternative, the base stations can detect the signal strength of the mobile computer. In such a case, the table would relate a known position of the mobile computer to the signal strength of the mobile computer at that location as detected by the one or more base stations.

Using a derived table of signal strength data to locate and track a user in a wireless network

Inferring location of a mobile computing device in an indoor environment and crowdsourcing radio frequency data are described herein. A location of the mobile computing device in the indoor environment is inferred based upon known physical constraints of the indoor environment and data from an inertial sensor of the mobile computing device, wherein an initial location in the indoor environment of the mobile computing device is unknown, a heading offset of the mobile computing device is arbitrary, and wherein a placement of the mobile computing device on a user is arbitrary.

Locating a mobile computing device in an indoor environment

One or more mobile devices and a cloud server computing device are utilized to provide Internet access to one or more client computing devices. One of the clients is designated as a Wi-Fi access point. The Wi-Fi access point implements a reverse-infrastructure Wi-Fi mode which tethers available mobile devices and other clients to the access point. The cloud server periodically computes an optimal number of the mobile devices to be used for data striping, and transmits a webpage to a requesting client by striping data associated with the webpage across the optimal number of the mobile devices.

Energy-efficient on-the-fly Wi-Fi hotspot using mobile devices

Embodiments of the claimed subject matter provide an approach to managing the power state of wireless network devices in a wireless network. Embodiments include a process for modifying the scheduling of data distribution device by referencing the data buffer in an access point for data intended for a wireless network client operating under a power-saving mode, and re-arranging the distribution queue of the access point to be granted priority for a time which coincides with the delivery of a polling beacon to the power-saving wireless client. Other embodiments include a method to create virtual access points for a particular usage, and to leverage access points in a wireless network for particular usages of devices in range.

Network assisted power management

A electronic communication system that can detect and adapt to delays in delivery of e-mail messages. The system may adapt to delays by using mechanisms other than the initially intended path to convey the information intended for delivery by e-mail. A possible adaptation is reprioritization of an e-mail message that is found to be experiencing a delay.

Electronic mail delay adaptation

The problem signature extraction technique extracts problem signatures from trace data collected from an application. The technique condenses the manifestation of a network, software or hardware problem into a compact signature, which could then be used to identify instances of the same problem in other trace data. For a network configuration, the technique uses as input a network-level packet trace of an application's communication and extracts from it a set of features. During the training phase, each application run is manually labeled as GOOD or BAD, depending on whether the run was successful or not. The technique then employs a learning technique to build a classification tree not only to distinguish between GOOD and BAD runs but to also sub-classify the BAD runs into different classes of failures. Once a classification tree has been learned, problem signatures are extracted by walking the tree, from the root to each leaf.

Learning signatures for application problems using trace data

Growing community of inventors

Bellevue, WA, United States of America

Venkata N Padmanabhan