Nokia Technologies Oy

Bayerische Motoren Werke Aktiengesellschaft

Nokia Corporation

Nokia Mobile Phones Limited

Core Wireless Licensing S.a.r.l.

B Ayerische M Otoren W Erke a Ktiengesellschaft

Wsou Investments, LLC

Jilei Tian

Huanhuan Cao

Yang Cao

Jani Kristian Nurminen

Victor Popa

Alvin Chin

Qing Li

Wei Wang

Sailesh Kumar Sathish

Feng Ding

Xia Wang

Jesper Olsen

Kasthuri Rengan Nallappa Soundararajan

Wangsu Hu

Clint Woker

Xinxing Yang

Ari Aarnio

Guohua Zhang

Yuezhong Tang

Xiaogang Yang

Rile Hu

Janne Suontausta

Happia Cao

Zhen Liu

Ying Liu

Hao Yang

Dong Hui Liu

Kuifei Yu

Hengshu Zhu

Wenfeng Li

Kari Laurila

Xiaojie Wang

Xu Jia

Imre Kiss

Olli Viikki

Jian Wang

Teemu Ahmaniemi

Ramalingam Hariharan

Juha H&auml;kkinen

Michael Karg

Won Tchoi

Péter Boda

Ákos Vétek

Longsheng Qian

Sascha Hamzehi

Xueying Li

Shaowei Jiang

Cheng Zeng

Liang Hong

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

Embodiments relate to a method, a computer program and a system for optimizing execution of natural language to structured query language. The method comprises the steps of&#x2014;receiving a natural language text input and performing natural language processing on the natural language text input to generate a plurality of encoded question tokens. Next, the method comprises performing natural language processing on a plurality of table schema stored in a database to generate a plurality of encoded table schema tokens for each table schema. Further, the method comprises determining a similarity between the plurality of encoded question tokens and the plurality of encoded table schema tokens for at least two table schemas of the plurality of table schema. Furthermore, the method comprises determining an output table schema from the plurality of table schema based on the similarity and outputting a natural language string based on the output table schema.

Natural language processing of encoded question tokens and encoded table schema based on similarity

The present disclosure relates to a concept for training one or more model parameters of a predictive parking difficulty model for different locations based on collected telemetry data. A ground truth ranking related to subjective parking difficulties at the different locations is obtained based on pairwise comparison of parking difficulties between pairs of the different locations by one or more humans. A prediction loss between a model ranking of the different locations obtained by the predictive parking difficulty model and the ground truth ranking is determined. The one or more model parameters are adjusted to minimize the prediction loss between the model ranking and the ground truth ranking.

Methods and apparatuses for training one or more model parameters of a predictive parking difficulty model

Examples relate to systems, method and systems, methods and computer programs for efficiently determining an order of driving destinations and for training a machine-learning model using distance-based input data. A system for determining an order of a plurality of driving destinations is configured to obtain information on a distance between the plurality of driving destinations, the distance being defined for a plurality of routes between the plurality of driving destinations, with the routes being defined separately in both directions between each combination of driving destinations of the plurality of driving destinations within the information on the distance. The system is configured to provide the information on the distance between the plurality of driving destinations as input to a machine-learning model, the machine-learning model being trained to output information on an order of the plurality of routes based on the information on the distance provided at the input of the machine-learning model. The system is configured to determine the information on the order of the plurality of driving destinations based on the information on the order of the plurality of routes.

Systems, methods, and computer programs for efficiently determining an order of driving destinations and for training a machine-learning model using distance-based input data

A method for providing responsive actions to user inputs in a multi-domain context includes receiving, by a speech-based user interface, a first speech input from a user and converting said first speech input into a text-based representation of the first speech input. A natural language processor processes the text-based representation to determine an intent, entity and internal state of the first speech input. The method further includes determining, by a model-based module based on the intent, entity and internal state, a first data processing policy to apply to the first speech input, wherein the first data processing policy is either a rules-based data processing policy applied by a rules-based module or a statistical model-based data processing policy applied by the model-based module. The first responsive action is generated by the determined first data processing module, and outputted via the speech-based user interface and/or a machine interface.

Hybrid policy dialogue manager for intelligent personal assistants

The present disclosure relates to a concept for machine-learning-based prediction of a destination for a user. Based on historic search data associated with the user, at least one candidate destination is determined based on the user and a given context. A plurality of embedding vectors are determined from an embedding matrix, wherein the embedding vectors are associated with the at least one candidate destination, the user, and the given context. The embedding matrix comprising embedding vectors for different components of the historic search data. The plurality of embedding vectors are fed into one or more first neural network layers to generate a semantic embedding for the candidate destination. The semantic embedding is into one or more second neural network layers to generate a probability score for the candidate destination.

Methods and apparatuses for predicting user destinations

A system, method, and non-transitory computer-readable medium are provided for implementing adaptive live trip prediction in a vehicle of a user in real-time while the user uses the vehicle. In addition to predicting trip destinations based on historical trip data, the system uses a confidence measure of each of a plurality of known candidate destinations and a rejection strategy for rejecting candidate destinations as possible destinations based on real-time vehicle location information. When the system rejects all of the known candidate destinations and determines that the user's destination is an unknown or new destination, the system determines the location of the new destination be repeatedly determining an angle between a line from the original location to the current location and a north direction over time until the new destination can be accurately predicted. Further, the system is able to self-optimize the prediction performance without an explicit interaction with the user.

Adaptive live trip prediction solution

A method for smartly managing a plurality of potential travel destinations of a user is provided. The method includes determining a plurality of similarities between different pairs of the plurality of potential travel destinations. Further, the method includes weighting the plurality of similarities using contextual information related to the user, and clustering part of the plurality of potential travel destinations based on the plurality of weighted similarities.

Method and apparatus for smartly managing a plurality of potential travel destinations of a user

An approach is provided for determining the context-aware similarity between applications. A similarity platform processes one or more context logs associated with one or more devices to determine one or more context intents. The similarity platform further determines one or more distributions of one or more applications executed at the one or more devices with respect to the one or more context intents. The similarity platform also determines a context-aware similarity between two or more applications based, at least in part, on the one or more application distributions.

Method and apparatus for determining context-aware similarity

The present disclosure relates to predicting a destination of a user's current travel path. Real-time travel data associated with the current travel path is generated, wherein the real-time travel data comprises the user's current location. A plurality of potential destinations of the current travel path are determined based on historic travel data associated with one or more historic travel paths of the user. At least one destination of the current travel path is predicted from the plurality of potential destinations based on tracking a distance from the user's current location to each of the plurality of potential destinations.

Methods and apparatuses for predicting a destination of a user's current travel path

The exemplary embodiments of the invention provide at least a method, apparatus and system to perform operations including receiving context data from an electronic device, causing, at least in part based on the received context data, an identification of at least one context model compatible with the electronic device, and causing, at least in part, provision of the electronic device with the at least one compatible context model. In addition, the exemplary embodiments of the invention further provide at least a method, apparatus and system to perform operations including causing, at least in part, a provision of context data associated with an electronic device to a context inference service, in response, receiving a context model from the context inference service, and causing adaptation of the received context model as a current context model of the electronic device.

Method and apparatus for context sensing inference

A fuel consumption prediction method and apparatus is provided. A respective historic fuel consumption is extracted from historic travel data for at least one historic travel path of a vehicle. At least one historic fuel-related quantity influencing the respective historic fuel consumption is extracted from the historic travel data for each historic travel path. Model parameters of a machine learning model for predicting fuel consumption is adjusted based on the extracted respective historic fuel consumption and the at least one historic fuel-related quantity for each historic travel path. The fuel consumption for a planned trip is predicted using the machine learning model with the adjusted model parameters.

Methods and apparatuses for fuel consumption prediction

A method and system for determining a preference of a user for a location is provided. The preference of the user for the location is determined based on a plurality of users and a plurality of locations. The method includes determining a user-location relation based on a plurality of relations of the users with the locations, determining a plurality of POItags indicative of one or more properties of the plurality of locations, and determining a user-POItag relation based on the plurality of users and the plurality of POItags. The method also includes determining a location-POItag relation based on the plurality of locations and the plurality of POItags, and determining the preference of the user for the location based on at least one of the user-location relation, the user-POItag relation, and the location-POItag relation. The system includes a controller configured to perform the method. A vehicle including the system is also provided.

Method and system for modeling user and location

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

An approach is provided for constructing dynamic latent models that determine consumer/social network intrinsic properties and automatically recommend user interactions with different social networks. A modeling platform determines one or more social networks associated with one or more users, one or more devices associated with the one or more users, or a combination thereof. A modeling platform processes and/or facilitates a processing of data associated with the one or more social networks to generate one or more latent models describing the one or more social networks. A modeling platform causes, at least in part, a presentation of a recommendation to interact with the one or more social networks, one or more other social networks, or a combination thereof based, at least in part, on the one or more latent models.

Method and apparatus for constructing latent social network models

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Beijing, China

Jilei Tian