Apple Inc.

Other

Robert Boschgmbh

Lukas Michael Marti

Ronald Keryuan Huang

Robert William Mayor

Shannon M Ma

Michael P Dal Santo

Lili Cao

Yefim Grosman

Glenn Donald MacGougan

Pejman Lotfali Kazemi

Jason Dere

Rudolph Van Der Merwe

David R Stites

John Kassebaum

Christopher G Nicholas

Marcel Van Os

Arun Godfrey Mathias

Bradford Allen Moore

Seejo K Pylappan

Christine B McGavran

Marlene Wan

Gregory Dennis Bolsinga

Xufeng Han

Rama Krishna Chitta

Mikhail Tsoupko-Sitnikov

Joakim Linde

Brent Michael Ledvina

Augustin Prats

Morgan J Grainger

Brian J Tucker

Stephen J Rhee

Jahshan A Bhatti

François M Jouaux

Kevin Duffy

Liviu Teodor Popescu

Christopher J Edmonds

Tadaaki Shimada

Xiaoyuan Tu

Kevin P McLaughlin

Matthaeus Krenn

Hyo Jeong Shin

Kjell Fredrik Larsson

Leonardo A Soto Matamala

Ross W Anderson

Srichand Pendyala

Jason J Gauci

Can Koc

Josh Redding

Weiyu Zhang

Craig M Hiller

Brian Stephen Smith

Patrick Thomas Dillon

Francois M Jouaux

Jay N Bruins

Adam M Driscoll

Jeffrey Michael Bartunek

Bradley Joel Jensen

Zehua Zhou

Jorge Sans Sangorrin

Weili Shao

Glenn Donald Macgougan

Systems, methods, and program products for providing services to a user by a mobile device based on the user's daily routine of movement. The mobile device determines whether a location cluster indicates a significant location for the user based on one or more hints that indicate an interest of the user in locations in the cluster. The mobile device can perform adaptive clustering to determine a size of area of the significant location based on how multiple locations converge in the location cluster. The mobile device can provide location-based services for calendar items, including predicting a time of arrival at an estimated location of a calendar item. The mobile device can provide various services related to a location of the mobile device or a significant location of the user through an application programming interface (API).

Determining a significant user location for providing location-based services

A method includes determining a routing graph based on a map, wherein the routing graph represents locations from the map; determining a routing horizon based on the routing graph, wherein the routing horizon defines a portion of the routing graph that is usable for routing; determining a cost-action structure that includes, for each location of the routing horizon, a cost-to-go value for travel from a respective location from the routing horizon to a destination, wherein the cost-to-go values implicitly describe an optimal route for travel from each of the respective locations from the routing horizon to the destination; receiving, from an autonomous agent, a request that specifies one of the locations from the routing horizon; and providing, to the autonomous agent, information from the cost-action structure based on the specified location from the routing horizon.

Route guidance system

A method includes obtaining a first map element from a first map, identifying second map elements from a second map based on locations of the second map elements relative to the first map element, and identifying first and second point on the second map elements based on proximity to beginning and ending points of the first map element. One or more of the second map elements define a corresponding portion of the second map between the first point and the second point. The method also includes determining a registration score for the first map element relative to the corresponding portion of the second map, and in response to determining that the registration score indicates a match between the first map element and the corresponding portion of the second map, defining registration information that describes a relationship between the first map element and the corresponding portion of the second map.

Offline registration of elements between maps

Methods, systems, and computer program products for determining transit routes through crowd-sourcing, for determining an estimated time of arrival (ETA) of a vehicle of the transit route at a given location, and for providing predictive reminders to a user for catching a vehicle of the transit route. A server receives signal source information about wireless signal sources detected by user devices, including information about a first wireless signal source detected by some devices. The server determines that the first wireless signal source is moving. The server determines that the first wireless signal source is associated with a public transit route upon determining that the signal source information satisfies one or more selection criteria. The server stores information associating the first wireless signal source with the public transit route as transit movement data corresponding to the public transit route.

Providing transit information

A mobile device can provide predictive user assistance based on various sensor readings, independently of or in addition to a location of the mobile device. The mobile device can determine a context of an event. The mobile device can store the context and a label of the event on a storage device. The label can be provided automatically by the mobile device or by the external system without user input. At a later time, the mobile device can match new sensor readings with the stored context. If a match is found, the mobile device can predict that the user is about to perform the action or recognize that the user has performed the action again. The mobile device can perform various operations, including, for example, providing user assistance, based on the prediction or recognition.

Context driven routine prediction assistance

A mobile device with a route prediction engine is provided that can predict current/future destinations or routes to destinations for the user, and can relay prediction information to the user. The engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on user-specific data. The user-specific data includes data about (1) previous destinations traveled, (2) previous routes taken, (3) locations of calendared events, (4) locations of events for which the user has electronic tickets, and/or (5) addresses parsed from e-mails and/or messages. The prediction engine relies on one or more of user-specific data stored on the device and data stored outside of the device by external devices/servers.

Mobile device with predictive routing engine

A device in an automated environment can detect patterns in the user's interactions with accessories in the automated environment and can provide feedback to the user based on the patterns. Examples include: suggesting automation of particular actions based on the patterns; suggesting actions that conform to the pattern when the user performs part of the pattern; or suggesting changes to a pattern to conform to a preferred pattern. A state of the group of accessory devices can be changed together based on the pattern of accessory state changes for the group of accessory devices.

Automated environment providing feedback based on user routine

A method includes determining a first route from a first location to a second location using a first map that includes first map elements, wherein the first route includes a series of the first map elements from the first map. The method also includes matching the series of the first map elements from the first route to second map elements from a second map to define a subset of the second map elements, and determining a second route from the first location to the second location using the subset of the second map elements. The second route is constrained to the subset of the second map elements and the second route includes a series of the second map elements from the subset of the second map elements. The method also includes outputting information describing the second route for at least one of storage or display.

Constrained registration of map information

A user interface enables a user to calibrate the position of a three dimensional model with a real-world environment represented by that model. Using a device's sensor, the device's location and orientation is determined. A video image of the device's environment is displayed on the device's display. The device overlays a representation of an object from a virtual reality model on the video image. The position of the overlaid representation is determined based on the device's location and orientation. In response to user input, the device adjusts a position of the overlaid representation relative to the video image.

Registration between actual mobile device position and environmental model

An automated environment can include an accessory device that operates according to an automation rule, to take a prescribed action when a triggering condition occurs. A controller device for the automated environment can determine a user's regular routine and can detect when the user is deviating from the regular routine. The controller device can communicate with accessory devices in the automated environment to modify their behavior relative to the automation rules.

Modification of automated environment behavior based on user routine

A method includes capturing images at a device and analyzing the images at the device using a first analysis model to obtain information regarding an observed object that corresponds to a predetermined object. The method also includes determining whether the information regarding the observed object is consistent with stored mapping information, and, in response to determining that the information regarding the observed object is not consistent with the stored mapping information, modifying the stored mapping information based on the observed object.

Distributed collection and verification of map information

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Santa Clara, CA, United States of America

Lukas Michael Marti