Bayerische Motoren Werke Aktiengesellschaft

Nokia Corporation

Nokia Technologies Oy

Alvin Chin

Jilei Tian

Kasthuri Rengan Nallappa Soundararajan

Clint Woker

Hao Wang

Yang Cao

Ari Aarnio

Qing Li

Xiaogang Yang

Michael Karg

Gregory Athas

Won Tchoi

Hrishikesh Brahmakal

Kasthuri Rengan Nallappasoundararajan

A method for smart parking a connected vehicle is disclosed. The method determines a destination of a user's vehicle based on the user's input or historic travel data of the user. The method receives information on available parking spots in a predefined area near the user's destination based on a parking database. The method receives an input to reserve a desired parking spot among the available parking spots. The method initiates an online check-in of the reserved parking spot in the parking database.

Method, device, cloud service, system, and computer program for smart parking a connected vehicle

Method, apparatus, system, computer program product and computer readable medium are disclosed for enhancing a user's engagement or experience with a service. The method comprises obtaining the user's current active level with respect to the service with respect to the service; determining a target active level; and sending the user a notification about the service at a time determined based on the user's current active level and the target active level, so that the user is notified of the service without being severely annoyed.

Method, apparatus and system for enhancing a user's engagement with a service

The disclosure is directed to determining an actual point-of-interest (POI). For example, using at least one computing device, a first set of probable POIs corresponding to a first geographical location and first POI category information may be determined. The at least one computing device may also be used to determine a second set of probable POIs corresponding to a second geographical location and determine second POI category information. By analyzing the first and second POI category information, the actual POI from the first and second set of probable POIs may be determined.

Method and system for determining an actual point-of-interest based on user activity and environment contexts

The disclosure is directed to improving driving experience by determining and providing a personalized estimated time of arrival (ETA). For example, using at least one computing device, driving data associated with a user may be collected and stored in memory. The at least one computing device may also be used to receive ETA-related data including one or more real time ETA estimations, perform analysis on one or more of the driving data and the ETA-related data so as to determine a set of personalized parameters of a mapping function that correspond to a training set. The at least one computing device may also be used to determine the personalized ETA based at least in part on the determined set of personalized parameters and provided to the user.

Method and system for determining and providing a personalized ETA with privacy preservation

The disclosure is directed to improving driving experience. For example, using one or more computing devices, a current location of a user may be determined and a place of interest, located at a distance from the current location, may be identified. The one or more computing devices may determine that the place of interest meets one or more requirements, and designate the place of interest as a candidate place of interest. The candidate place of interest may be recommended to the user based on a defined criteria. The one or more computing devices may also determine that the user has arrived at a parking location corresponding to the candidate place of interest, and associate the parking location and the candidate place of interest based on one or more inputs by the user.

Method and system for contextually recommending a place of interest to a user and smart check-in

An approach is provided for determining one or more recommendations based on an incremental update of a recommendation model. An incremental platform causes, at least in part, a categorization of activity information into a plurality of subsets based, at least in part, on one or more user groups, one or more item groups, at least one update time for at least one recommendation model associated with the activity information, or a combination thereof. The incremental platform further processes and/or facilitates a processing of the activity information based, at least in part, on the categorization to determine whether to perform an incremental update of the at least one recommendation model. The incremental platform also determines one or more recommendations based, at least in part, on the at least one recommendation model, the incremental update, or a combination thereof.

Growing community of inventors

Chicago, IL, United States of America

Alvin Chin