Microsoft Technology Licensing, LLC

Apple Inc.

Other

Daniel R Delling

Renato F Werneck

Andrew V Goldberg

Dennis Schieferdecker

Ronald Keryuan Huang

Brian J Andrich

Richard B Warren

Lili Cao

Hyo Jeong Shin

Hengbin Luo

Patrick James Coleman

Ethan T Bold

Michael Wegner

Rami Khawandi

Thomas Pajor

Moises Goldszmidt

Edith Cohen

Philomena F Lobo

Saurabh V Pendse

Ilya Razenshteyn

Christian Sommer

John Charles Krumm

Andreas Georg Nowatzyk

Xiang Zhao

Mihai Budiu

Christopher John Leggetter

Ittai Abraham

Yunhong Zhou

Xia Sharon Wan

Darshan Vishwanath Kantak

Supratik Maitra

Moritz Helge Kobitzsch

Naiping Liu

Fabian Fuchs

Renato Fonseca Furquim Werneck

In some implementations, a computing device can proactively determine a destination and request traffic information for routes from a starting location to the destination. In some implementations, a computing device can identify some routes between a starting location and a destination as non-recommended routes and recommend other routes. In some implementations, a computing device can rank routes between a starting location and a destination based on automatically-determined user interest. In some implementations, a computing device can determine a user is familiar with a route and adjust the information presented to the user about the route accordingly.

Providing light navigation guidance

Presenting suggested routes based on local route ranking

Presenting non-recommended routes

A route-biased search in a mobile navigation system locates points of interest (POI) on or convenient to a mobile user's current travel. The route-biased search operates in two modes, navigation and non-navigation. The navigation mode identifies the most convenient POIs that honor the mobile user's route to their destination. The non-navigation mode identifies the most convenient POIs that are located ahead of the user based on the user's inferred direction of travel. The route-biased search determines the best possible routes to the identified POIs taking into account the impact of the detour costs of traveling to the POI along with other ranking factors.

Route-biased search

Aspects of the subject disclosure are directed towards managing sponsored online content based upon advertiser behavior. Defining mini-markets to represent such advertiser behavior may be accomplished by clustering queries that generate revenue from one or more campaigns. Query revenue data between queries and a set of campaigns may be used to determine such mini-markets. To illustrate, a query whose highest revenue is attributed to a campaign may be selected for that campaign's mini-market. When that query is entered as a search term, the campaign's mini-market helps allocate space for advertisements.

Estimating similarity of nodes using all-distances sketches

Customizable route planning is a technique for computing point-to-point shortest paths in road networks. It includes three phases: preprocessing, metric customization, and queries. A graphics processing unit may be used, e.g., in the metric customization phase, to make customization even faster, enabling a wide range of applications including highly dynamic applications and on-line personalized cost functions.

Customizable route planning using graphics processing unit

Computing shortcuts for cells used in cell-based routing in a mobile navigation system re-computes shortcuts based on updated traffic conditions and estimated time of arrival (ETA) at an entry to the cell. Shortcuts are computed on demand and stored in a last recently used (LRU) cache. Shortcuts are computed using cost functions stored in the LRU cache. Shortcuts are computed in accordance with metadata stored in the LRU cache. Shortcuts are optionally based on predicted ETA and future traffic conditions to provide accurate estimates of best cost routes.

On-demand shortcut computation for routing

Live traffic routing in a mobile navigation system generates alternate routes based on the user's current location, live traffic conditions and estimated time of arrival at certain locations along the route. Live traffic routing re-ranks alternate routes taking into account which routes offer the best cost path to the user's destination.

Live traffic routing

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

Distance query techniques are provided that are robust to network structure, scale to large and massive networks, and are fast, straightforward, and efficient. A hierarchical hub labeling (HHL) technique is described to determine a distance between two nodes or vertices on a network. The HHL technique provides indexing by ordering vertices by importance, then transforming the ordering into an index, which enables fast exact shortest-path distance queries. The index may be compressed without sacrificing its correctness.

Distance queries on massive networks

A graph that includes multiple nodes and edges is received. Multiple instances of the graph are generated by randomly instantiating the edges according to either a binary independent cascade model or a randomized edge length independent cascade model. Where the binary independent cascade model is used, combined reachability sketches are generated for each node across all instances of the graph. Where the randomized edge length independent cascade model is used, combined all-distances sketches are generated for each node across all instances of the graph. Depending on which model is used, the combined reachability or all-distances sketches are used to estimate the influence of nodes in the graph or to estimate a subset of nodes from a graph of a specified size with a maximum influence using a greedy algorithm.

Estimating influence using sketches

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Sunnyvale, CA, United States of America

Daniel R Delling