Patent application number | Description | Published |
20130080371 | CONTENT RECOMMENDATION SYSTEM - A system and method for in-vehicle content recommendation is disclosed. The system comprises an aggregator module, a modeler module and a recommendation module. The aggregator module aggregates content data describing content such as a music, a news program, a podcast, an audio book, a movie, a television program, etc. The aggregator module also aggregates situation data describing an environment in which the content was recommended to a user, and feedback data describing the user's response to the recommendation. The modeler module generates a content preference model. The content preference model represents the user's dynamic content preferences based at least in part on the aggregated data. The recommendation module generates a first content recommendation based at least in part on the content preference model. | 03-28-2013 |
20130090751 | Media Volume Control System - A system and method for regulating media volume is disclosed. The system comprises a prediction engine and a regulation module. The prediction engine collects sensor data and scene category data. The scene category data includes data describing an environment in which the media volume is regulated. The prediction engine predicts a workload for a user based at least in part on the scene category data and the sensor data and generates a predicted workload value for the predicted workload. The regulation module adjusts the media volume based at least in part on the predicted workload value. | 04-11-2013 |
20130151148 | Place Affinity Estimation - A system and method for estimating a place affinity for a user is disclosed. The system comprises a gathering module, a communication module and a scoring module. The gathering module receives a place and retrieves rich place data associated with the place. The communication module retrieves user profile data associated with a user and a place affinity model associated with the user. The scoring module estimates an affinity score for the place using the place affinity model based at least in part on the rich place data and the user profile data. | 06-13-2013 |
20130158854 | Navigation System - A system and method for estimating journey destinations is disclosed. The system comprises a driving history module, a frequency module, a duration module, a direction module, a metric module and a quality module. The driving history module retrieves a set of learning parameters including driver history data describing one or more past journeys. The frequency module analyzes the learning parameters to determine a candidate set including data describing frequent start locations and frequent end locations for one or more potential journeys to one or more destinations. The duration module estimates journey duration data for the one or more potential journeys. The direction module estimates direction data describing one or more directions for the one or more potential journeys. The metric module determines one or more metrics for the one or more potential journeys. The quality module determines one or more quality scores for the one or more potential journeys. | 06-20-2013 |
20130158855 | Journey Learning System - A system and method for estimating journey destinations is disclosed. The system comprises a conversion module, a frequency module, a metric module, a quality module and a summary module. The conversion module converts a set of driver history data to a set of learning parameters. The frequency module analyzes the set of learning parameters and current journey data to generate estimated journey data describing one or more potential journeys. The metric module analyzes the estimated journey data and the set of current status data to determine one or more metrics associated with the estimated journey data. The quality module determines one or more quality scores associated with the estimated journey data. The summary module determines one or more status summaries and one or more estimate summaries. The summary module associates the one or more status summaries and the one or more estimate summaries with the estimated journey data. | 06-20-2013 |
20140180526 | Autonomous Navigation Through Obstacles - A system and method for navigating a mobile automated system through obstacles is disclosed. The system includes a communication module, an estimation module, a density module, a vector module, a navigating module and a command module. The communication module receives image sensor data from one or more sensors. The estimation module estimates one or more obstacle parameters. The density module determines a left obstacle image density and a right obstacle image density for a path from a start point to a navigating destination based on the one or more obstacle parameters. The vector module generates a vector model to determine a navigating direction based on the left obstacle image density and the right obstacle image density. The navigating module determines a navigating velocity for navigating the mobile automated system to the navigating destination. The command module generates one or more navigating commands based on the navigating velocity. | 06-26-2014 |
20140201004 | Managing Interactive In-Vehicle Advertisements - The disclosure includes technology for managing interactive advertisements for users. The technology includes an example system including a processor and a memory storing instructions that when executed cause the system to: determine user data associated with a user; determine contextual data associated with the user; determine an interactive advertisement for presentation to the user in a vehicle based on the user data and the contextual data; and present the interactive advertisement to the user in the vehicle. | 07-17-2014 |
20140355879 | Computationally Efficient Scene Classification - The disclosure describes novel technology for inferring scenes from images. In one example, the technology includes a system that can determine partition regions from one or more factors that are independent of the image data, for an image depicting a scene; receive image data including pixels forming the image; classify pixels of the image into one or more pixel types based on one or more pixel-level features; determine, for each partition region, a set of pixel characteristic data describing a portion of the image included in the partition region based on the one or more pixel types of pixels in the partition region; and classify a scene of the image based on the set of pixel characteristic data of each of the partition regions. | 12-04-2014 |