Abstract: The system described herein provides a sensor-based assessment of attention interruptibility. In a driving scenario, this system solves a persistent safety issue with regard to dangerous interruptions as they increase driver workload and reduce performance on the primary driving task. Being able to identify when a driver is interruptible is critical for building systems that can mediate these interruptions. The present invention utilizes sensor data collected from wearable devices and on-board diagnostics which can be used as input to build a machine learning classifier that can determine driver interruptibility in real-time.

Abstract: A data processing system for structuring data includes a repository storing data referencing sources, the data representing actions and states for each of the sources. A data structuring engine generates a data sequence, for each source. Each data sequence represents states and actions for the source. An aggregation engine aggregates data sequences for the sources into a data structure, each entry of the data structure representing a state. Each entry includes an identifier for the state, a probability value of at least one action occurring at the state, the probability value based on a proportion of the data sequences comprising the state that also comprises the at least one action, and frequency data indicating a proportion of the data sequences included in the entry. A classification engine traverses the data structure and classifies the data sequences into groups based on probability values and frequency data associated with each state of each respective data sequence.

Abstract: A system method and device for predicting navigational decision-making behavior is provided. The location of a device is used to generate the current location of the device, and a history of past locations of the device. A behavior predicting application predicts the route a drive is going to travel based on driver preferences, such as left-hand turns, avoidance of bridges, etc., as well as the preferred destinations and/or routes traveled in the past. The preferences of a particular driver can be learned by the system to be implemented in the behavior predicting application, or manually input by the driver. The system can employ GPS or other positioning technology to determine the location of the device. The device can be a standard GPS-enabled in-car device, or other portable device such as a cell phone or PDA.

Abstract: A system method and device for predicting navigational decision-making behavior is provided. The location of a device is used to generate the current location of the device, and a history of past locations of the device. A behavior predicting application predicts the route a drive is going to travel based on driver preferences, such as left-hand turns, avoidance of bridges, etc., as well as the preferred destinations and/or routes traveled in the past. The preferences of a particular driver can be learned by the system to be implemented in the behavior predicting application, or manually input by the driver. The system can employ GPS or other positioning technology to determine the location of the device. The device can be a standard GPS-enabled in-car device, or other portable device such as a cell phone or PDA.