Microsoft Technology Licensing, LLC

Nokia Corporation

Nokia Technologies Oy

David L Racz

Yiming Ma

Rich Hankins

Eric J Horvitz

Moises Goldszmidt

Petri Piippo

Balinder Malhi

Paul B Koch

Tuomas Vaittinen

Jacek A Korycki

Jason Robert Wither

Carmen Esther Au

Joshua Jenkins

Srividya Mohan

Dave Lickorish

Dawn Woodard

Manuel Ocampo

Galina Nogin

Ray William Rischpater

Systems, methods, and software are disclosed herein for enhancing entity classification operations for digital maps. In an implementation, an entity classification system associates tiles in a grid overlaying a map with discrete positioning records produced by devices operating in areas represented in the map. For each tile in an area of interest in the grid, the system produces a scalar description based on a subset of the discrete positioning records associated with the tile. The system then performs a binary classification of each tile as a type of entity (e.g. a road, business, or residence) based on the scalar description of the tile and the scalar descriptions of other tiles in the area of interest.

Classifying entities in digital maps using discrete non-trace positioning data

A system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model including latent variables that are associated with the trip. The machine learning model may be trained from historical trip data that is based on location-based measurements reported from mobile devices. Once trained, the machine learning model may be utilized for predicting variability of travel time. A process may include receiving an origin, a destination, and a start time associated with a trip, obtaining candidate routes that run from the origin to the destination, and predicting, based at least in part on the machine learning model, a probability distribution of travel time for individual ones of the candidate routes. One or more routes may be recommended based on the predicted probability distribution, and a measure of travel time for the recommended route(s) may be provided.

Predicting and utilizing variability of travel times in mapping services

A system and method for detecting and preventing PSTN fraud and abuse in real time includes a detection engine and a call management engine. The system includes at least one user record, at least one call data record, at least one fraud score record, and at least one remediation record. A call management engine enables users to establish VoIP calls connections to destination phone numbers. A fraud detection engine screens VoIP call connections to detect potential fraud and generates fraud records and remediation records when potential fraud is detected. The fraud detection engine may additionally direct the call management to terminate a VoIP call connection.

Detecting and preventing fraud and abuse in real time

Techniques to derive or predict location context for a user of a mobile device include receiving signal data that indicates a set of one or more distinct signal sources from which signals are received at the mobile device for each of multiple different times. The method further comprises determining whether the mobile device is moving outside a specified area at a current time based on the signal data. The method further comprises incrementing a count for a stationary state associated with the set of one or more distinct signal sources at the current time, if the mobile device is determined to be not moving outside the specified area. The method also comprises delivering a service to the mobile device based on the stationary state.

Method and apparatus for incrementally determining location context

An apparatus, method, computer program and user interface where the apparatus includes at least one processor; and at least one memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, enable the apparatus to: display a perspective view of a geographical location at a first scale in a first region of a display; identify a point of interest associated with the geographical location; and display the identified point of interest at a second scale in a second region of the display.

Apparatus, method and computer program for displaying points of interest

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Palo Alto, CA, United States of America

David L Racz