Abstract: Embodiments are directed towards providing a system that provides an environment in which multiple user applications can be executed in the background of a vehicle head unit without direct interaction by a user of the head unit. A category-management application is configured to communicate with the user applications. The category-management application receives a request for content. The category-management application provides a request to one or more of the user applications to cause the user applications to execute as background processes to fulfill the request. The category-management application receives responses from the user applications. The category-management application selects and presents content associated with the fulfilled request to the user.

Abstract: Embodiments are directed towards providing a system that presents customized content to a user of an automobile based on what the user of the automobile is looking at. A first camera captures images of the user of the interior of the automobile, and a second camera captures images of a scene that the user is viewing. The images are analyzed to determine if the user is looking at, or not looking at, an object in the scene. If the user is looking at such an object, content associated with the object is selected and presented to the user.

Abstract: Embodiments are directed towards providing a system that presents customized content to a user in an automobile based on the usage patterns of the automobile or its current occupant. Accessory data from the automobile is monitored to learn the automobile-usage patterns. The automobile-usage patterns are mapped to a plurality of content items, such as by mapping the content items to services, which are then mapped to the automobile-usage patterns. The plurality of content items are prioritized for display to the user based on the automobile-usage patterns and the frequency of those automobile-usage patterns. The prioritized content items can be presented to the user sequentially or in response to accessory data that matches an automobile-usage pattern.

Abstract: One embodiment of the present invention predicts a vehicular event relating to machinal performance using information obtained from interior and exterior sensors, vehicle onboard computer (“VOC”), and cloud data. The process of predication is able to activate interior and exterior sensors mounted on a vehicle operated by a driver for obtaining current data relating to external surroundings, interior settings, and internal mechanical conditions of the vehicle. After forwarding the current data to VOC to generate a current vehicle status representing real-time vehicle performance in accordance with the current data, retrieving a historical data associated with the vehicle including mechanical condition is retrieved. In one aspect, a normal condition signal is issued when the current vehicle status does not satisfy with the optimal condition based on the historical data. Alternatively, a race car condition is issued when the current vehicle status meets with the optimal condition.

Abstract: Technologies are disclosed herein for selecting one or more content instances of a plurality of content instances for display on a head unit of a vehicle. The content instances correspond to a location of a vehicle and are received by the head unit. The head unit obtains vehicle specific information from memory of the head unit. Based on the vehicle specific information, the head unit selects the one or more content instances using a set of criteria for determining which, if any, content instances to display. Selection of the one or more content instances using the set of criteria may involve consideration of information associated with individual instances of the plurality of content instances. The selected one or more content instances are displayed on a display of the head unit.

Abstract: A method and/or system is able to provide driver fingerprint via metadata extraction managed by a driver rating (“DR”) model trained by a machine learning center (“MLC”) coupled to a cloud based network (“CBN”). In one embodiment, a DR system includes a set of outward facing cameras, a set of inward facing cameras, and a vehicle onboard computer (“VOC”). The set of outward facing cameras mounted on a vehicle is used to collect external images representing a surrounding environment in which the vehicle operates. The set of inward facing cameras mounted in the vehicle is used to collect internal images including operator body expression representing at least operator's attention. The VOC is configured to determine the identity of operator and current operating style in response to the collected internal images, the collected external images, and historical stored data.

Abstract: Embodiments are directed towards a smart sign that provides dynamic content auctions based on device-specific information obtained from mobile devices in proximity of the smart sign. Device-information requirements and content are received from content providers. The device-information requirements include target device-specific information and a price that the content provider is willing to pay to have its content displayed when the target device-specific information is received from a mobile device. Device-specific information is received from each mobile device in the proximity of the smart sign. The auction for the smart sign is performed by comparing the device-specific information to the device-information requirements for the content providers. Third-party content, e.g., advertisement, is selected for the content provider that paid a highest amount of money for the device-information requirements that match the device-specific information.

Abstract: A method or system is able to refine Global Positioning System (“GPS”) information for guiding a vehicle via extracted metadata using a GPS refinement (“GR”) model managed by a virtuous cycle containing sensors, machine learning center (“MLC”), and a cloud based network (“CBN”). The GR system includes a set of outward facing cameras, a vehicle onboard computer (“VOC”), and GR model. The outward facing cameras mounted on a vehicle are capable of collecting external images representing a surrounding environment in which the vehicle operates. The VOC is configured to generate a positional vehicle location with respect to the surrounding environment in accordance with the external images and historical stored data obtained from CBN. The GR model is configured to generate a driving guidance based on combined information between the positional vehicle location and GPS data.

Abstract: Embodiments are directed towards establishing a network between mobile devices, an automobile head unit, and a plurality of automobile accessories. A user utilizes a user interface on a mobile device to send an accessory access request to the head unit. The head unit receives the request and determines if the mobile device is authentic. If authentic, the head unit determines if the mobile device has the proper permissions to perform the requested access of the accessory. If permitted, the head unit generates and sends control commands to the accessory or obtains the requested accessory data and provides it to the mobile device.

Abstract: Embodiments are directed towards providing a dynamic application environment where separate components of an application can execute on separate processing hardware at any given point in time. A host device monitors current operating characteristics associated with a computing device, such a head unit of an automobile, and based on those characteristics selects which components of one or more applications to execute on the computing device and which components to execute on the host device, if any. The host device provides the selected components to the computing device for execution by the computing device and the host device executes any other components that are not executed by the computing device. The host device monitors the current operating characteristics associated with the computing device, and modifies the selection of which components are executing on which device based on changes in current operating characteristics.

Abstract: A method and system for enabling use of a reusable-code section across applications of different operating systems. For a first application designed for a first operating system (“OS”), a first native-code section and a reusable-code section are defined as part of the first application. For a second application designed for a second OS, a second native-code section and the reusable-code section are defined as part of the second application, and the second application is incapable of running on the first OS. The first and second native-code sections communicate with a web-services server, which communicates with a plurality of backend-services servers, at least some of which are controlled by different entities. The web-services server coordinates the execution of service requests with the backend-services servers on behalf of the first and second applications. The reusable-code section facilitates the display of user-interface elements when the first and second applications are executed.

Abstract: A method and/or system is able to provide driver fingerprint via metadata extraction managed by a driver rating (“DR”) model trained by a machine learning center (“MLC”) coupled to a cloud based network (“CBN”). In one embodiment, a DR system includes a set of outward facing cameras, a set of inward facing cameras, and a vehicle onboard computer (“VOC”). The set of outward facing cameras mounted on a vehicle is used to collect external images representing a surrounding environment in which the vehicle operates. The set of inward facing cameras mounted in the vehicle is used to collect internal images including operator body expression representing at least operator's attention. The VOC is configured to determine the identity of operator and current operating style in response to the collected internal images, the collected external images, and historical stored data.

Abstract: A presentation system is described herein. The system can include a room for playing a scene and can be made up of a collection of displays. The system can also include media players to manage the playing of content on a corresponding display of the collection of displays. The system can also include a server to distribute objects to the media players. The scene can include the objects, which may be selectively played on the displays when the scene is played on the room. The media players can receive the objects and can determine whether the objects are intended to be played on the corresponding displays as part of the scene to be played. If so, the objects can be rendered on the corresponding displays on which the objects are intended to be played, which can bypass the rendering of the scene as a whole by the system.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system is able to refine Global Positioning System (“GPS”) information for guiding a vehicle via extracted metadata using a GPS refinement (“GR”) model managed by a virtuous cycle containing sensors, machine learning center (“MLC”), and a cloud based network (“CBN”). The GR system includes a set of outward facing cameras, a vehicle onboard computer (“VOC”), and GR model. The outward facing cameras mounted on a vehicle are capable of collecting external images representing a surrounding environment in which the vehicle operates. The VOC is configured to generate a positional vehicle location with respect to the surrounding environment in accordance with the external images and historical stored data obtained from CBN. The GR model is configured to generate a driving guidance based on combined information between the positional vehicle location and GPS data.

Abstract: A method or system that is able to adjust an exterior mirror of a vehicle via an automatic mirror-setting (“AM”) model managed by a virtuous cycle containing a cloud based network (“CBN”). The system includes a set of mirrors, a set of inward facing cameras, a vehicle onboard computer (“VOC”), and AM module. In one embodiment, the mirrors, attached to a vehicle, are configured to capture at least a portion of the external environment in which the vehicle operates. The inward facing cameras, mounted in the vehicle, are configured to collect internal images, including the operator's facial features showing operator visual characteristics. The VOC, which is coupled to the CBN, is configured to determine operator vision metadata based on the internal images, operator visual characteristics, and historical stored data. The AM module is able to adaptively set a mirror to an optimal orientation so that the area of the external blind spot is minimized.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A system includes a plurality of cameras each enabled to operate independently of the others as a component camera. The system includes logic operable to form the component cameras into a network operable as a single federated camera device. The lens types of the component cameras are non-heterogeneous, and the federated camera forms a virtual lens comprising characteristics combining the lens types of the component cameras.

Abstract: A system includes an analytical router including an I/O interface to a plurality of external devices or sensors; logic to identify a device id and a time or location for measurements or feeds received from the plurality of external devices or sensors; logic to apply the device id to associate the measurements or feeds with a device id grouping of the measurements or feeds; logic to apply the time or location, or both, to associate the measurements or feeds with a temporal or geo grouping of the measurements or feeds; and a web page generator to generate a web page comprising one or more links pointing to the measurements or feeds for at least one of the temporal or geo grouping of the measurements or feeds and the device id grouping of the measurements or feeds.

Abstract: Embodiments are directed towards establishing a network connection between a mobile device and an automobile head unit. A user of the mobile device interacts with a user interface on the mobile device to establish the connection with the head unit. If authorized, the head unit disconnects a currently connected device if the connection is to be via classic Bluetooth protocols. If the head unit has reached a maximum number of registered mobile devices, then the head unit sends a request to the mobile device for a selection of which currently registered mobile devices are to be unregistered. The head unit registers the mobile device with the head unit and establishes the connection between the mobile device and the head unit via classic Bluetooth protocols or Bluetooth Low Energy protocols depending on the type of connection request.