Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Roger Shane Barga

David Bruce Lomet

Gaurav D Ghare

Jonathan David Goldstein

Stelios Paparizos

Vinay Gupta

Anoop Gupta

Raymond Edward Ozzie

Mary P Czerwinski

Gueorgui Chkodrov

Pavel Curtis

Richard J McAniff

Gur Kimchi

Paul F Ringseth

Tihomir Tsvetkov Tarnavski

William J Vass

Andy Shen

Zachary Apter

Eric J Horvitz

Doug Burger

Bruce Alan Johnson

Paul J Hough

Gerhard Weikum

Mingsheng Hong

Henricus Johannes Maria Meijer

Marvin Michael Theimer

Michael Isard

Alexander Sasha Stojanovic

Carl M Carter-Schwendler

Rebecca Ann Norlander

Mushegh Malkhasyan

Gourav Roy

Fusheng Yuan

Mohamed Hassan Ali

Savas Parastatidis

Nelson Sampaio Araujo, Jr

Bin Zhu

Chetan Banthiya

Ritesh Singh

Aditya Krishnan

Dmitry Berkovich

Iftach Ragoler

Mirek Riedewald

Paul Andrew Lafranchise

Swarnaprakash Udayakumar

Brandon Kyle Pelfrey

Jason Chen Hooi Chionh

Jared J Jackson

Di Guo

Automated reconfiguration of real time data stream processing may be implemented. A processing function that describes one or more operations to be performed with respect to one or more data streams may be executed at one or more processing nodes. Performance metrics describing the performance of the processing function at the processing nodes may be collected and monitored. A reconfiguration event may be detected for the processing function. A different execution configuration for the processing function may be determined and initiated in response to detecting the reconfiguration event.

Automated reconfiguration of real time data stream processing

A robotic device management service obtains, from a customer, a first set of parameters of a robotic device and a second set of parameters for a simulation environment for testing a robotic device application installable on the robotic device. The set of parameters are used to indicate a storage location of the application and a selection of a simulation environment for testing the application. In response to the request, the robotic device management service selects a set of resources on which to execute the simulation in the simulation environment. The robotic device management service obtains the robotic device application from the storage location and loads the application on to the set of resources to execute the simulation.

Robotics application development architecture

An event detection system for detecting events related to vehicles based on sensor data from the vehicles. An event detection service on a network may receive streams of sensor data from vehicles and apply machine learning techniques to train models and to analyze the data to detect vehicle-related events. Supervised and unsupervised techniques may be used to train the models and to detect known as well as anomalous events. Events may be detected based on data from one or from multiple vehicles. The system may respond to events by alerting entities and/or signaling vehicle control systems to respond to the events. Trained models may be downloaded to vehicles; the vehicle computer systems can then locally detect events. The downloaded models can be further trained on the vehicles, uploaded to the network, and integrated into the network-based models to improve the performance of the models.

Network-based vehicle event detection system

Disclosed are various embodiments for a framework for time-associated data stream storage, processing, and replication. A plurality of streams of time-associated data are received from a plurality of sources via a network using an application-layer protocol. Each of the plurality of streams is divided into a plurality of fragments. An acknowledgement is sent to each of the plurality of sources for each of the plurality of fragments via the network using the application-layer protocol. Processing is performed on each of the plurality of fragments for individual ones of the plurality of streams. An action is implemented relative to a respective fragment based at least in part on a result of the processing.

Framework for time-associated data stream storage, processing, and replication

A single device that receives communications, resources, data, and/or other information intended for a user having multiple contact addresses is provided. The single device can receive various information intended for multiple telephone numbers, email aliases, screen names, aliases, other means of contact, or combinations thereof. The device can selectively forward the information to the user based on a current user role, user preferences, contact information, or based on other criteria. If information is to be forwarded to the user at substantially the same time as receipt, automatic means of notifying the user of the role for which the communication is intended are provided.

Single device with multiple personas

Real time detection of anomalies may be implemented for a data stream. A data stream may receive data records as input. An evaluation of the data records may be performed as the data records are received utilizing an anomaly detection model that is dynamically generated for a time period that includes the data records. A responsive action may be performed in response to detecting various anomalies, such as providing a notification of the anomaly, requesting a corrective action, invoking further analysis of the detected anomaly. Evaluations of the data record and responsive actions may be performed as specified by a user, in some embodiments.

Real time anomaly detection for data streams

Managed function execution for processing data streams in real time may be. A function that describes one or more operations to be performed with respect to one or more data streams may be received via programmatic interface for a managed stream processing system. Stream processing nodes capable of applying the function may be determined and execution of the one or more operations may be initiated at the stream processing nodes as data records of the data stream are received. Results of the application of the processing function may be provided to one or more destinations specified for the function. Performance metrics may also be collected for the execution of the function and provided to a client that submitted the function.

Managed function execution for processing data streams in real time

Various embodiments are described for systems and methods for managing data. The system may include a device group configured for peer-to-peer communications, the device group including a computing device and one or more peer computing devices. The system includes a cross device application programming interface (API) that is implemented as a device group API client executed on the computing device and each of the peer computing devices. Each device group API client includes a permissions module that is configured to determine whether a request satisfies a device-group-specific permission for access to data stored on any device associated with the device group. Upon authorization of the request, a file storage module is configured to retrieve and output the requested file.

Computing system for managing data

A server computing device and related method for providing recommendations to a user computing device are disclosed. In one example, user activity of the user of a user computing device and friend activity of a friend using a friend computing device are received. A request for a recommendation is received from the user device. Based on the user and friend activities, it is estimated that the user and friend will approach an intersecting location within a window of time, and the user and friend are available to meet in that window. A recommendation of a service offered within a distance of the location is sent, with the recommendation displayed on the user computing device.

Server computing device for recommending meeting a friend at a service location based on current location, travel direction, and calendar activity

A long-term personal agent program, executable as network service and/or on one or more user computing devices and related method for identifying opportunities and making recommendations on behalf of one or more users, are disclosed herein. In one example, the personal agent program includes a monitoring engine configured to monitor and interpret a user's activities over time with a plurality of sensing and logging methodologies according to user authorization, the use of statistical methods for learning to understand a user's goals and behavioral patterns from data, and the use of procedures for computing the expected value of information guiding sensing and logging in different contexts. The personal agent further may include a recommendation methodology configured to make suggestions and to take actions on behalf of the user, in the present moment as well as for future times, based on inferences about user goals and opportunities in the world.

User computing device with personal agent program for recommending meeting a friend at a service location based on current location, travel direction, and calendar activity

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Woodinville, WA, United States of America

Roger Shane Barga