Abstract: One or more devices in a data analysis computing system may be configured to receive and analyze movement data and determine driving trips based on the received data. The driving trips may be used along with the movement data to authenticate drivers based on a determined driver profile.

Abstract: Aspects of the disclosure relate to processing remotely captured sensor data. A computing platform having at least one processor, a communication interface, and memory may receive, via the communication interface, from a user computing device, sensor data captured by the user computing device using one or more sensors built into the user computing device. Subsequently, the computing platform may analyze the sensor data received from the user computing device by executing one or more data processing modules. Then, the computing platform may generate trip record data based on analyzing the sensor data received from the user computing device and may store the trip record data in a trip record database. In addition, the computing platform may generate user record data based on analyzing the sensor data received from the user computing device and may store the user record data in a user record database.

Abstract: Systems and methods for utilizing statistical relational learning techniques in order to predict factors for nodes of a node graph, such as a node graph that represents attacks and incidents to a computing system, are described. In some embodiments, the systems and methods identify certain nodes (of a node graph) as representing malicious attributes of an email or other threat artifact received by a computing system or network and utilize relational learning to predict the maliciousness of attributes represented by other nodes (of the node graph).

Abstract: A microwave oven container may include a body for containing a liquid, an agitator projecting through a bottom wall of the body to agitate the liquid contained in the body, where the agitator includes a first end located in the body and a second end located exterior to the body. The second end of the agitator is able to couple with a turntable drive of a microwave oven to power movement of the agitator in response to rotation of the turntable drive. The container may also include a cage located within the body that substantially surrounds the first end of the agitator and includes a plurality of openings to allow liquid contained in the body to pass through the cage.

Abstract: An turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Abstract: Systems and methods for utilizing statistical relational learning techniques in order to predict factors for nodes of a node graph, such as a node graph that represents attacks and incidents to a computing system, are described. In some embodiments, the systems and methods identify certain nodes (of a node graph) as representing malicious attributes of an email or other threat artifact received by a computing system or network and utilize relational learning to predict the maliciousness of attributes represented by other nodes (of the node graph).

Abstract: An turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Abstract: Systems, methods, and computer-readable media, are disclosed in which a variety of data describing the condition of an object can be obtained and probabilistic likelihoods of causes and/or value of damages to the object can be calculated. In a variety of embodiments, data obtained from third-party systems can be utilized in these calculations. Any of a number of machine classifiers can be utilized to generate the probabilistic likelihoods and confidence metrics in the calculated liabilities. A variety of user interfaces for efficiently obtaining and visualizing the object, the surrounding geographic conditions, and/or the probabilistic likelihoods can further be utilized as appropriate to the requirements of specific applications of embodiments of the invention.

Abstract: One or more mobile device movement detection computing devices and methods are disclosed herein based on acceleration data collected from an accelerometer of a mobile device found within an interior of a vehicle. The mobile device movement detection computing devices may identify a likely mobile device movement event based on a change of angle between two three-dimensional acceleration vectors. Where the mobile device movement detection computing devices detect a likely mobile device movement event, sensor data from various sensors of a mobile device are collected and aggregated for a window of time encompassing the mobile device movement event. Data from vehicle sensors and other external systems may also be used. The mobile device movement detection computing devices calculate a risk score based on the aggregates sensor data, and provide feedback to a mobile device or vehicle based on the calculated risk score.

Abstract: Systems and methods for utilizing statistical relational learning techniques in order to predict factors for nodes of a node graph, such as a node graph that represents attacks and incidents to a computing system, are described. In some embodiments, the systems and methods identify certain nodes (of a node graph) as representing malicious attributes of an email or other threat artifact received by a computing system or network and utilize relational learning to predict the maliciousness of attributes represented by other nodes (of the node graph).

Abstract: One or more devices in a data analysis computing system may be configured to receive and analyze movement data and determine driving trips based on the received data. The driving trips may be used along with the movement data to authenticate drivers based on a determined driver profile.

Abstract: One or more devices in a data analysis computing system may be configured to receive and analyze movement data and determine driving trips based on the received data. The driving trips may be used along with the movement data to authenticate drivers based on a determined driver profile.

Abstract: An turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Abstract: An turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Abstract: One or more devices in a data analysis computing system may be configured to receive and analyze movement data and determine driving trips based on the received data. The driving trips may be used along with the movement data to authenticate drivers based on a determined driver profile.

Abstract: One or more mobile device movement detection computing devices and methods are disclosed herein based on acceleration data collected from an accelerometer of a mobile device found within an interior of a vehicle. The mobile device movement detection computing devices may identify a likely mobile device movement event based on a change of angle between two three-dimensional acceleration vectors. Where the mobile device movement detection computing devices detect a likely mobile device movement event, sensor data from various sensors of a mobile device are collected and aggregated for a window of time encompassing the mobile device movement event. Data from vehicle sensors and other external systems may also be used. The mobile device movement detection computing devices calculate a risk score based on the aggregates sensor data, and provide feedback to a mobile device or vehicle based on the calculated risk score.

Abstract: An turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Abstract: An turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Abstract: An turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Abstract: One or more mobile device movement detection computing devices and methods are disclosed herein based on acceleration data collected from an accelerometer of a mobile device found within an interior of a vehicle. The mobile device movement detection computing devices may identify a likely mobile device movement event based on a change of angle between two three-dimensional acceleration vectors. Where the mobile device movement detection computing devices detect a likely mobile device movement event, sensor data from various sensors of a mobile device are collected and aggregated for a window of time encompassing the mobile device movement event. Data from vehicle sensors and other external systems may also be used. The mobile device movement detection computing devices calculate a risk score based on the aggregates sensor data, and provide feedback to a mobile device or vehicle based on the calculated risk score.