Abstract: One embodiment of the present invention is able to provide an interactive parking management (“IPM”) in accordance with information obtained from interior and exterior sensors, vehicle onboard computer (“VOC”), and cloud data. The process, in one embodiment, is capable of acknowledging a parking activity initiated by a vehicle traveling in a geographic location via a communications network. Upon providing parking information to the vehicle based on the data obtained from the current parking status, historical parking status, and big data for facilitating the parking activity, the movement of the vehicle is monitored in accordance with the current parking status. After recording a physical location where the vehicle is parked and parking duration, a parking invoice is generated in response to information relating to the recorded information such as physical location and parking duration.

Abstract: One embodiment of the present invention discloses a process of providing a report predicting potential risks relating to an operator driving a vehicle using information obtained from various interior and exterior sensors, vehicle onboard computer (“VOC”), and cloud network. After activating interior and exterior sensors mounted on a vehicle operated by a driver for obtaining data relating to external surroundings and internal environment, the data is forwarded to VOC for generating a current fingerprint associated with the driver. The current fingerprint represents current driving status in accordance with the collected real-time data. Upon uploading the current fingerprint to the cloud via a communications network, a historical fingerprint which represents historical driving information associated with the driver is retrieved.

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: 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 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 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 external environment in which the vehicle operates. The inward facing cameras, mounted in the vehicle, are configured to collect internal images including operator facial features showing operator visual characteristics. VOC, which is coupled to 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 area of external blind spot is minimized.

Abstract: A method and/or system is able to improve vehicle safety via metadata extraction by an inferred attentional (“IA”) model trained by a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the system includes a set of outward facing camera, inward facing cameras, and vehicle onboard computer (“VOC”). The outward facing cameras collect external images representing a surrounding environment in which the vehicle operates. The inward facing cameras collect internal images including operator facial expression representing at least operator's attention. The VOC is configured to identify operator's attention in response to the collected internal images and the collected external images.

Abstract: A system includes sensor nodes each including camera and a processor, the nodes interconnected via a wide area network. A master controller establishes named queues each associated with a proximate physical object in an area including one or more of the sensor nodes and detectable by the sensor node camera. Each sensor node identifies a viewed physical object in a feed from the sensor node camera, and associates the viewed physical object with one of the named queues.

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 capable of managing automobile parking space (“APS”) using containerized sensors, machine learning center, and cloud based network is disclosed. A process, in one aspect, monitors the surrounding information observed by a set of onboard sensors of a vehicle as the vehicle is in motion. After selectively recording the surrounding information in accordance with instructions from a containerized APS model which is received from a machine learning center, an APS and APS surrounding information are detected when the vehicle is in a parked condition. Upon rewinding recorded surrounding information leading up to the detection of APS, labeled data associated with APS is generated based on APS and the recorded surrounding information. The process subsequently uploads the labeled data to the cloud based network for facilitating APS model training at the machine learning center via a virtuous cycle.

Abstract: A system includes event engine logic distributed across a plurality of devices dispersed over an area of coverage, and logic to process events according to layout information homologized by an event web service from scene captures and location data from the plurality of devices, and logic to partition the homologized layout into areas of coverage and to assign each area of coverage to a device or subgroup of devices in the plurality of devices.

Abstract: A method includes capturing a first image at a first time using a camera; processing the first image to identify a representation of an object in the first image; storing the first image as an event forming a navigable marker in a conference room tracking system; measuring one or more of a room sound and a room activity level at a time associated with capture of the first image; generating a heat marker representing a level of the room sound or the room activity level; and storing the heat marker in association with the event.

Abstract: A machine system includes monitor devices each having a camera, the monitor devices distributed over a physical area; layout logic forms images from the cameras of the monitor devices into a scene layout for the area; user interface logic receives training signals from sensors directed to a person physically present in the area and to correlate those signals to subareas of the layout; and analytical logic analyzes the layout and training signals to ascertain subareas of the area at which the monitor devices should focus machine sensor and processing resources.

Abstract: A camera includes logic to capture images and video (collectively, ‘media’) and to store the captured media internally to the camera in an encrypted format; the encrypted format including multiple regions encrypted with different keys, each key corresponding to a human subject or object identified in the media.

Abstract: A system includes event engine logic distributed across a plurality of devices dispersed over an area of coverage, and logic to process events according to layout information homologized by an event web service from scene captures and location data from the plurality of devices, and logic to partition the homologized layout into areas of coverage and to assign each area of coverage to a device or subgroup of devices in the plurality of devices.

Abstract: A room conferencing system includes a hub with a plurality of sensor nodes, each sensor node including a sensor node camera and a sensor node processor. A master controller is operable to establish a plurality of named queues, each of the named queues associated with a proximate physical object in an area comprising the hub and detectable by the sensor node camera. Each sensor node operable to cause the sensor node processor to identify a viewed physical object in a feed from the sensor node camera, and associate the viewed physical object with one of the named queues.

Abstract: A system includes digital media rendering devices and logic to federate the plurality of devices into a single federated rendering device for a proximate mobile device. The system accepts a request from the mobile device for rendering services by the federated rendering device via a rendering protocol.