Apple Inc.

Curbside Inc.

Rakuten Group, Inc.

Other

Seejo K Pylappan

Marcel Van Os

Bradford Allen Moore

Christopher Blumenberg

Jaron Waldman

Stephen Olivier Lemay

Emanuele Vulcano

Denis Laprise

Gregory Dennis Bolsinga

Christine B McGavran

Scott James Forstall

Jonathan Anderson Bennett

Richard Williamson

Lukas Michael Marti

Michael P Dal Santo

Christopher D Moore

James Alexander Howard

Harold O Habeck

Stephane Lunati

Ronald Keryuan Huang

Billy P Chen

Devrim Varoglu

Patrick Steven Piemonte

Jason A Skinder

Ken Goto

Brian Dote

Markus Fischer

Michael James Matas

Xiaoyuan Tu

Mike Matas

Nikhil M Bhatt

Aroon Pahwa

Megan M Frost

Chris Blumenberg

Alexander David Wallace

Justin O'Beirne

Matthew B Ball

François M Jouaux

Cédric J Bray

Joseph J Stevens

Per Fahlberg

Nils Stefan Daniel Bengtsson

Ravi Nikhil Bhatt

Mira Nicole Bhatt

Joshua P Kannankunni

Clayton D Stevens

Logan Alastair John Wallace

Olivia May Wallace

Harry Chen

Exemplary methods, apparatuses, and systems read location data representing a current location of a mobile device. The location data for the mobile device is taken at a first sampling rate. Upon determining that the location data indicates that the mobile device is approaching a geographic area having a history of wireless communication loss, the mobile device is triggered to increase location data sampling from the first sampling rate to a second sampling rate.

Mitigating signal loss and directional ambiguity with dynamic location data sampling

Exemplary methods, apparatuses, and systems receive an indication that an order associated with a user account is ready for pickup. One or more messages to a user device associated with the user account are transmitted in response to the received indication that the order is ready. The one or more messages include identification of the order as being ready for pickup, a pickup location for the order, and a threshold distance from the pickup location for the order. Location data is received at a limited interval from a user device while the user device is outside of the threshold distance from the pickup location. Location data is received at a more frequent interval than the limited interval while the user device is within the threshold distance. The location data is transmitted to a local pickup device to trigger an alert when the user device is approaching the pickup location.

Limited location tracking of a user device for local pickup

A context-aware voice guidance method is provided that interacts with other voice services of a user device. The voice guidance does not provide audible guidance while the user is making a verbal request to any of the voice-activated services. Instead, the voice guidance transcribes its output on the screen while the verbal requests from the user are received. In some embodiments, the voice guidance only provides a short warning sound to get the user's attention while the user is speaking on a phone call or another voice-activated service is providing audible response to the user's inquires. The voice guidance in some embodiments distinguishes between music that can be ducked and spoken words, for example from an audiobook, that the user wants to pause instead of being skipped. The voice guidance ducks music but pauses spoken words of an audio book in order to provide voice guidance to the user.

Context-aware voice guidance

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

Systems, methods and computer-readable media are disclosed for adjustment in the amount of calculations performed at a server for determining a location of a device for purposes of providing an arrival alert to a destination of arrival of the device. In one aspect, a method includes determining a zone around a destination; receiving location information from a tracking device that is traveling to the destination; based on the location information and the zone, determining whether the tracking device is within the zone to yield a determination; determining a remaining time for a user associated with the tracking device to reach the destination based on the determination; and providing an arrival alert to the destination regarding an arrival time of the user at the destination based on the remaining time and an arrival threshold.

Smart location determination for arrival estimation and generation of arrival alerts

The present technology discloses methods, non-transitory computer-readable media, and systems for generating, for a transaction, a transaction code, wherein the transaction code includes a temporary key allowing access to a vehicle at a delivery location; sending, to a merchant device, the transaction code; sending, to the merchant device, an alert that the vehicle has arrived at the delivery location, wherein the temporary key is used to access the vehicle for placing a product inside the vehicle; receiving a notification that the product has been placed inside the vehicle; and disabling the temporary key.

System and method for device tracking and contactless order delivery and pick up

Some embodiments of the invention provide a novel prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for a user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following: (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user. In some embodiments, the prediction engine only relies on user-specific data stored on the device on which this engine executes. Alternatively, in other embodiments, it relies only on user-specific data stored outside of the device by external devices/servers. In still other embodiments, the prediction engine relies on user-specific data stored both by the device and by other devices/servers.

Warning for frequently traveled trips based on traffic

Systems, methods and computer-readable medium are provided for improving location determination of mobile devices and using the location information obtained via such improved methods to track content conversion. In one aspect, a computer-implemented method includes receiving a request from a third party to track at least one content; determining a reference point for at least one tracking device; activating at least one sensor on-board the at least one tracking device, the at least one tracking device determining displacement information of the at least one tracking device based on the reference point and data collected by the at least one sensor; receiving the displacement information from the at least one tracking device; identifying locations visited by the at least one tracking device based on the displacement information; and determining a content conversion rate for the at least one content based on the locations visited by the at least one tracking device.

Content conversion tracking based on location data

Systems, methods and computer-readable medium are provided using on-board sensor data of a tracking device in addition to satellite signals to improve the determination of the location of a tracking device. In one aspect, a method includes detecting, at a server, presence of a tracking device in a high error zone; determining if geographical coordinates of a current location of the tracking device in the high error zone are within a threshold of registered geographical coordinates of the current location; activating at least one sensor on-board the tracking device when the geographical coordinates are not within the threshold; receiving displacement information of the tracking device from the tracking device; determining an updated location of the tracking device based on the current location and the displacement information; and providing an arrival alert to a destination toward which the tracking device is traveling based on the updated location.

Augmented location determination using sensor data

Systems, methods and computer-readable media are obtaining location updates of a tracking device via peer to peer communication with other nearby devices where obtaining such update is otherwise not possible. In one aspect, a method includes receiving, at a device, a first location update from a network component; determining, by the device, if a triggering condition is met for determining a second location update; activating, by the device, device-to-device communication for receiving the second location update from nearby devices in vicinity of the device when the triggering condition is met receiving the second location update from at least one of the nearby devices via the device-to-device communication; and sending the second location update to a server for the server to determine an arrival alert for alerting a destination of an amount of time remaining until the device reaches the destination.

Peer to peer location updates

The present disclosure is directed to providing smart signaling of location updates. A method includes receiving a first location update from a tracking device; determining a remaining time for the tracking device to reach a destination location; determining whether the remaining time is within a threshold time of a timing of an arrival alert to be sent to a device associated with the destination location; based on whether the remaining time is within the threshold time or not, configuring the tracking device to send location updates to a server in one of a continuous reporting mode or a significant reporting mode, wherein the tracking device is configured to send the location updates to the server less frequently in the significant reporting mode compared to the continuous reporting mode; and determining when to send the arrival alert based on the location updates received from the tracking device.

Smart signaling of location updates for arrival estimation and generation of arrival alerts

The present disclosure enables an improved location determination and subsequent content providing. In one aspect, a method includes assigning a geographical tile to a tracking device, the geographical tile representing a geographical location in which the tracking device is currently located; activating at least one sensor on-board the tracking device, the at least one sensor of the tracking device being used to collect location data and determine displacement information of the tracking device on the tracking device; receiving the displacement information as well as at least one satellite based location update from the tracking device; determining at least one zone of interest in the tile at which the tracking device is currently located based on the displacement information and at least one satellite based location information of the tracking device; and providing at least one content associated with the at least one zone of interest to the tracking device.

Content providing based on location determination using sensor data

Growing community of inventors

San Francisco, CA, United States of America

Seejo K Pylappan