Microsoft Technology Licensing, LLC

Uber Technologies, Inc.

Microsoft Corproration

Microsoft Corporation

John Charles Krumm

Eric J Horvitz

Brian R Meyers

Ivan Jelev Tashev

Alice Jane Bernheim Brush

Ruston Panabaker

Jeffrey Darren Couckuyt

Neil Warren Black

Kentaro Toyama

Michael Lewis Seltzer

Jaime Brooks Teevan

Amy Kathleen Karlson

Barry L Brumitt

Ramaswamy Hariharan

Shahriyar J Amini

Gregory Ray Smith

Stephen C Harris

John Carlton Platt

Nissanka Arachchige Bodhi Priyantha

Gerald Cermak

Steven A N Shafer

Edward C Miller

Adel Amin AbdelAzim Youssef

Peng Chang

Ranveer Chandra

Timothy Mark Moore

Patrick Markus Baudisch

Warren Vincent Barkley

Stefan Saroiu

Mohammad Shabbir Alam

Paul E Newson

Yinghua Yao

Murugesan S Subramani

Xia Zhou

Barrett Brumitt

Florin Teodorescu

Julia Maureen Letchner

Richard G Campbell

Geoffrey Edward Pease

Madhurima Pawar

Nissanka Arachige Bodhi Priyantha

Kenneth Paul Hinckley

Stephen Edward Hodges

Lili Cheng

Matthew Graham Dyor

James William Scott

Paul A Viola

Aman Kansal

Zhaowei Charlie Jiang

Roland Lucas Fernandez

Jyh-Han Lin

Moises Goldszmidt

Eric I Horvitz

Daniel R Delling

Dimitris Achlioptas

Susan D Woolf

David J Marsh

Andrew V Goldberg

Scott Joseph Counts

Chandrasekhar Thota

David Ross Buerer

Steve Glenner

Jordan L K Schwartz

Vivek Bhanu

Ajoy Nandi

Paul B Koch

Christopher Jon Lang

Greg R Smith

Robert Warren Gruen

Melissa Nicole Lim

Steve Lombardi

Anurag Sharma

Denise L Chen

Adel Amin Youssef

Jason T Creighton

Dany Rouhana

Piali Choudhury

Arjun Sundararajan

Wayne G King

Jessica Kristan Wolk

Albert D Jee

Matthew Man Chung Cheung

Mark Huyler

Tim McGrath

Barry L Brummitt

Renato Fonseca Furquim Werneck

Amol Wankhede

Erc J Horvitz

Mangesh Deshpande

Ming-Wei Chang

Michael Lews Seltzer

Adel Amin Abdel Azim Youssef

Lili Cao

Lakshmi N Mummidi

Steven An Shafer

Geoffrey E Pease

Gerald F Cermak

Jason Timothy Creighton

Paul Koch

Mohammed Shabbir Alam

A method includes receiving one or more search terms at a mobile computing device while the mobile computing device is located at a particular location. A search query that includes the one or more search terms and a location history of the mobile computing device is transmitted to a server. The method also includes receiving one or more search results in response to the search query, where the one or more search results include content identified based on a predicted destination of the mobile computing device. An interface identifying the one or more search results is displayed at the mobile computing device.

Mobile search based on predicted location

The minimization of the amount of power consumed by an electronic device in acquiring or maintaining network connectivity with a network may extend the battery life of the electronic device. When the electronic device has established a communication connection with a wireless access point, the electronic device cycles a network interface controller of the electronic device between a power on state and a power off state without terminating the communication connection. Accordingly, the electronic device powers on a main processor of the electronic device when the network interface controller detects a beacon during the power on state that indicates the wireless access point has a buffered data frame for the electronic device.

Energy efficient network connectivity maximization

The claimed subject matter provides systems and/or methods that facilitate inferring probability distributions over the destinations and/or routes of a user, from observations about context and partial trajectories of a trip. Destinations of a trip are based on at least one of a prior and a likelihood based at least in part on the received input data. The destination estimator component can use one or more of a personal destinations prior, time of day and day of week, a ground cover prior, driving efficiency associated with candidate locations, and a trip time likelihood to probabilistically predict the destination. In addition, data gathered from a population about the likelihood of visiting previously unvisited locations and the spatial configuration of such locations may be used to enhance the predictions of destinations and routes.

Methods for predicting destinations from partial trajectories employing open- and closed-world modeling methods

An expected value of a measurement in a first context may be inferred based at least partly on a contextual signal. The contextual signal may comprise an actual value that is: (i) of a same type as the expected value, and (ii) associated with a second context that is different from the first context (e.g., the contexts can comprise geographical areas), or the contextual signal may comprise an actual value that is: (i) of a different type than a type of the expected value, and (ii) associated with the first context, or a second context that is different from the first context. If a difference between the expected value and an actual value of the first context is greater than a threshold difference, this condition is considered an anomaly. A detected anomaly may be used to determine an event that may be significant or otherwise of interest to a user community.

Localized anomaly detection using contextual signals

The subject disclosure is directed towards resolving an uncertain transportation context by suggesting one or more potential diversions. An automated routing system may generate routing information that includes map data as well as a location of a diversion having an expected cost in compliance with the uncertain transportation context. Such a diversion may be a waypoint that satisfies one or more user needs given an unknown/uncertain destination.

Planning under destination uncertainty

Described are methods that utilize a geographic location technology (e.g., GPS) to determine user location data, and existing network-based websites (e.g., Internet websites) for searching and accessing data related to the location data such that the user context can be developed and stored. A location component is provided that determines location data of a wireless communications device of a user. A context component is provided that accesses context data based on the location data to define a context in which the device is located. Activities, goals, and overall context of a user can be inferred through statistical fusion of multiple sources of evidence. The context data is presented to the user via the wireless device such that the user can make decisions as to where to go, for example. User preferences can be accessed and applied to filter context data according to what the user desires to see and access.

Integration of location logs, GPS signals, and spatial resources for identifying user activities, goals, and context

Driving history of a user with regard to a particular road intersection can be collected and retained in storage. A Markov model can be used to predict likelihood of the user making a particular decision regarding the intersection. A highest likelihood decision can be identified and used to create a travel route. In addition, contextual information can be taken into account when creating the route, such as time of day, road conditions, user situation, and the like.

Route prediction

Energy efficient maximization of network connectivity

One or more techniques and/or systems are provided for controlling a travel route planning module associated with a user device. Travel related data, for a user and regarding previously traveled routes by the user, may be indicative of user travel preferences and/or behaviors. The travel related data is evaluated against computed routes derived from different weighting values applied to travel metrics (e.g., a cost associated with a U-turn, a highway, an industrial zone, etc.). For example, weighting values may be iteratively adjusted to generate a plurality of modified computed routes that may be evaluated to identify a target computed route having a similarity to a previously traveled route of the user above a threshold. User preference weighted travel metrics, generated based upon weighted travel metrics of the target computed route, are used to control a travel route planning module to generate a customized travel route for the user.

Controlling travel route planning module based upon user travel preference

Calendar and scheduling applications organize and display user events and provide event reminders. A method of triggering a reminder includes accessing a reminder which describes an event. An individual associated with the event, other than the user, is identified. A future time is predicted when the user will be within proximity of the individual. A time to trigger the reminder for presentation to the user is determined, such that the time to trigger the reminder occurs in advance of the predicted future time when the user will be within proximity of the individual. Based on a determination that the time for triggering the reminder has occurred, the reminder is triggered to be presented to the user.

Intelligent surfacing of reminders

Systems, methods, and devices are described for implementing map matching techniques relating to measured location data. Probabilistic models, including temporal Bayesian network models and Hidden Markov Models, may be used for combining multiple classes of evidence relating to potential locations of points traversed on routes over time. Multiple route segments and overall routes may be maintained under relative uncertainty as candidates. The candidate route segments and overall routes may then be reduced into a smaller number of candidates or a single most likely route as a trip progresses. As the trip progresses, route segments in proximity to each location point are identified and candidate matches are determined. A probability of an entity traversing a candidate match at a given time and a probability of an entity traversing between a first candidate match at a first time and a second candidate match at a second time are determined based on a plurality of factors. Different modalities may be used to measure and transmit the location data.

Probabilistic map matching from a plurality of observational and contextual factors

The subject disclosure is directed towards automatically labeling location-related information such as corresponding to GPS data or the like with a semantic label. A classifier trained with machine learning is provided with feature data corresponding to the location-related information and other features, such as user demographics data of a person associated with location-related information. The semantic label is received from the classifier, and associated with the location-related information. Other features may be used, such as other egocentric features corresponding to a person's particular visit, features from a sequence of visits, and/or features from other user information. The semantic label may be used to trigger an action, label a location on a map or the like, and so on.

Semantic place labels

Growing community of inventors

Redmond, WA, United States of America

John Charles Krumm