Abstract: Embodiments relate to a method, computer program and system for improving a user experience. The computer-implemented method for improving a user experience inside a vehicle comprises determining an environmental context inside the vehicle, wherein the environmental context is assigned a plurality of actions, which influences the environmental context. Further, the method comprises choosing at least one action of the plurality of actions for improving the user experience.

Abstract: 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.

Abstract: 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—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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments relate to a method, computer program and system for improving a user experience. The computer-implemented method for improving a user experience inside a vehicle comprises determining an environmental context inside the vehicle, wherein the environmental context is assigned a plurality of actions, which influences the environmental context. Further, the method comprises choosing at least one action of the plurality of actions for improving the user experience.

Abstract: Embodiments relate to a method, a computer program and a device for controlling a route connecting a plurality of destinations in a road network. The method for controlling a route connecting a plurality of destinations in a road network comprises receiving input information. The input information comprises information about a situation with impact on the route. Furthermore, the method comprises recalculating a plurality of travel times between the plurality of destinations of the route, based on the received input information and distances between the plurality of destinations. Also, the method comprises providing output information about the recalculation, which comprises information about a dynamic change of the route.

Abstract: 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.

Abstract: A computer-implemented method for allocating charging stations including a) receiving charging station data, the charging station data identifying one or more charging stations and charging station locations; b) receiving one or more requests to charge one or more requesting vehicles, each request including data identifying a departure location and an arrival location of the respective requesting vehicle; c) receiving and/or determining detours data, the detours data indicating at least one charging station detour distance for a plurality of vehicles, the plurality of vehicles including the requesting vehicles; d) identifying charging station candidates for the requesting vehicles by optimizing an objective function with linear constraints; and e) allocating at least some of the charging station candidates to the requesting vehicles; wherein optimizing the objective function with linear constraints includes reducing a waiting time in the queue, price, a total detour distance for the requesting vehicles.

Abstract: Embodiments relate to a method, a computer program and a system for optimizing execution of natural language to structured query language. The method for optimizing execution of natural language to structured query language, comprises the steps of 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. Further 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 of the plurality of table schema and 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.

Abstract: 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.

Abstract: Examples relate to apparatuses, methods and computer programs for a user device and for a server, e.g. to provide a personalized service enabled by converting human language to processing logic. An apparatus for a user device comprises processing circuitry configured to obtain a user command from the user via a user interface of the user device, the user command being obtained in natural language. The processing circuitry is configured to provide the user command in natural language to the server via an interface. The processing circuitry is configured to obtain a response to the user command from the server via the interface, the response to the user command comprising an identifier that is assigned to the user command by the server. The processing circuitry is configured to assign the user command to an element of a graphical user interface of an application being executed by the user device, and to link the identifier to the element of the graphical user interface.

Abstract: Embodiments provide a method, a machine readable medium, a device and a vehicle for determining a route connecting a plurality of destinations in a road network, and a method, a machine readable medium and a device for training a machine learning module for determining a route connecting a plurality of destinations in a road network. The method for determining a route connecting a plurality of destinations in a road network comprises receiving input information, which comprises information on distances between the plurality of destinations in the road network. The method further comprises inputting the input information on the distances into a trained machine learning module and providing output information on the route connecting the plurality of destinations based on an output of the trained machine learning module.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.