Abstract: A method and/or computer program product automatically toggles insurance policy provisions for a self-driving vehicle (SDV) based on an operational mode of the SDV. One or more processor(s) receive an electronic signal indicating an operational mode of an SDV in real-time, where operational mode is either an autonomous mode or a manual mode. A first insurance policy provision provides coverage while the SDV is operating in the autonomous mode, and a second insurance policy provision provides coverage while the SDV is operating in the manual mode. The processor(s) monitor the SDV for a change in real-time to the operational mode of the SDV. In response to detecting a change in real-time to the operational mode of the SDV, the processor(s) dynamically toggle activation of the first and second insurance policy provisions consistent with the change in real-time to the operational mode of the SDV.

Abstract: A method controls an operational mode of a self-driving vehicle (SDV). One or more physical detectors detect an erratically driven vehicle (EDV) that is being operated in an unsafe manner within a predetermined distance of an SDV that is initially being operated in an evasive autonomous mode. One or more processors retrieve traffic pattern data for other SDVs, and examine the traffic pattern data to 1) determine a first traffic flow of the other SDVs while operating in the evasive autonomous mode, and 2) determine a second traffic flow of the other SDVs while operating in a manual mode. In response to determining that the first traffic flow has a higher accident rate than the second traffic flow, an operational mode device changes the operational mode of the SDV from the evasive autonomous mode to the manual mode.

Abstract: Aspects generate customized routes by automatically selecting between human-powered and motorized transport option segments, by determining a well-being objective of a traveler and an amount of the human-powered transport option for the traveler to perform to meet requirements of the well-being objective. Aspects generate recommended routes to a destination from a current location of the traveler that meets the well-being objective by selecting between route segment portions of usage of a human-powered option and of a motorized transport option, and include a first segment of usage of the human-powered transport option in response to determining that said included first segment requires an amount of effort that meets the well-being objective and does not exceed an amount of the human-powered transport option that the traveler is currently able to perform.

Abstract: Disclosed is a novel system and method for indicating a probability of errors in multimedia content. The system determines a user state or possible user distraction level. The user distraction level is indicated in the multimedia content. In one example, work is monitored being performed on the multimedia content. Distractions are identified while the work is being monitored. A probability of errors is calculated in at least one location of the multimedia content by on the distractions that have been identified. Annotations are used to indicate of the probability of errors. In another example, the calculating of probability includes using a function F(U,S,P) based on a combination of: i) a determination of user state (U), ii) a determination of sensitivity (S) of user input, and iii) a determination of user characteristics stored in a profile (P).

Abstract: A method and/or computer program product identifies and/or predicts a cognitive state of a user. A buffer on a device is loaded with a predetermined set of sensor readings for a user. A hardware receiver receives a “push” signal from the device. The “push” signal is transmitted by the user in response to the user subjectively experiencing a user-defined cognitive state. The “push” signal causes readings from the buffer to be loaded as pushed sensor readings onto a data matrix. One or more processors analyze the pushed sensor readings to identify a sensor pattern of the pushed sensor readings. In response to detecting a subsequent set of sensor readings that match the sensor pattern of the pushed sensor readings, a signal transmitter transmits a cognitive state signal that indicates a prediction of a particular cognitive state of the user.

Abstract: Systems and methods for dynamically managing figments are disclosed. A computer-implemented method includes: receiving, by a computing device, a question from a user; answering, by the computing device, the question using a first degree figment; classifying, by the computing device, the question based on topics; forwarding, by the computing device, the question to a set of second degree figments; receiving, by the computing device, answers to the question from the set of second degree figments; ranking, by the computing device, the answers received from the set of second degree figments; and providing, by the computing device, the ranked answers to the user.

Abstract: Systems and methods for dynamically managing figments are disclosed. A computer-implemented method includes: receiving, by a computing device, a question from a user; answering, by the computing device, the question using a first degree figment; classifying, by the computing device, the question based on topics; forwarding, by the computing device, the question to a set of second degree figments; receiving, by the computing device, answers to the question from the set of second degree figments; ranking, by the computing device, the answers received from the set of second degree figments; and providing, by the computing device, the ranked answers to the user.

Abstract: Providing a registry of sensor devices may comprise obtaining a device, determining one or more information types returned by the device, determining one or more communication protocols used by the device for transmitting information, determining one or more encoding schemes used by the device to format the information, adding the device to the registry of sensor devices including at least the one or more information types, the one or more communication protocols and the one or more encoding schemes, and allowing access to the registry of sensor devices.

Abstract: Providing a registry of sensor devices may comprise obtaining a device, determining one or more information types returned by the device, determining one or more communication protocols used by the device for transmitting information, determining one or more encoding schemes used by the device to format the information, adding the device to the registry of sensor devices including at least the one or more information types, the one or more communication protocols and the one or more encoding schemes, and allowing access to the registry of sensor devices.

Abstract: A system, method and computer program product for automatically providing a dynamically updated optimized itinerary (continuous optimization in real-time) which allows one to complete a specified task and also to complete other tasks from a to-do list, providing the user both with a packing list of resources to carry and a list of tasks that must be completed before leaving. The system and method also tracks the time and user's location to provide user with a new itinerary in the event a given task cannot be completed successfully. The method also provides an alert in case no practicable itinerary exists.

Abstract: A graphical interface module may provide a set of graphical presentations comprising at least: a Likelihood of Delivery chart showing a probability distribution of predicted delivery dates; a Delivery Date Risk Trend chart showing how the completion time for the project predicted according to the Likelihood of Delivery chart has changed over time; and a Burndown chart that shows at least work-items of planned work for the project. Each of the Likelihood of Delivery chart, the Delivery Date Risk Trend chart, and the Burndown chart has a timeline axis.

Abstract: An input is selected from a set of inputs used by a prediction model to produce an initial predicted value of an outcome. A changed predicted value of the outcome is produced by removing the selected input from the inputs to the model. An actual value of the outcome is obtained. A label residual is computed using the actual value and the changed predicted value. A second prediction model is formed to predict a value of the selected input. A variable residual is computed using an actual value and the predicted value of the selected input. An expression is generated of a plot of the label residual and the variable residual. The selected input is transformed, to form a transformed selected input, where the model produces a second predicted value of the outcome by using the transformed selected input.

Abstract: A computer-implemented method, system, and/or computer program product automatically provides spatial separation between a self-driving vehicle (SDV) operating in an autonomous mode and another vehicle on a roadway. One or more processors receive an emotional state descriptor for at least one occupant of the SDV and determine an emotional state of the occupant(s) of the SDV. A vehicle detector on the SDV detects another vehicle and the size of the other vehicle within a predefined proximity of the SDV. A control mechanisms controller determines that a field of view for the occupants in the SDV is blocked by the size of the other vehicle. The processor(s) issue spatial separation instructions to the control mechanisms controller on the SDV to adjust a spacing between the SDV and the other vehicle based on the emotional state descriptor for the occupant(s) in the SDV and the field of view for the occupant(s) in the SDV that is blocked by the size of the other vehicle.

Abstract: A computer-implemented method, system, and/or computer program product automatically provides spatial separation between a self-driving vehicle (SDV) operating in an autonomous mode and another vehicle on a roadway. One or more processors receive an emotional state descriptor for at least one occupant of the SDV and determine an emotional state of the occupant(s) of the SDV. A vehicle detector on the SDV detects another vehicle within a predefined proximity of the SDV. The processor(s) determine the braking abilities of the SDV and issue spatial separation instructions to a control mechanisms controller on the SDV to adjust a spacing between the SDV and the other vehicle based on the emotional state of the occupant(s) in the SDV.

Abstract: A method, computer system, and/or computer program product dynamically adjusts an insurance policy parameter for a self-driving vehicle (SDV) operating in manual mode. One or more processors receive a copy of manual control signals from an SDV, where the SDV is in manual mode during a particular time period. The processor(s) also receive a copy of computer control signals generated by an SDV on-board computer on the SDV during the particular time period, and compare the manual control signals to the computer control signals. In response to the manual control signals matching the computer control signals within a predetermined range, the processor(s) adjust an insurance policy parameter for the SDV while the SDV is being controlled by the particular human operator.

Abstract: A method, system, and/or computer program product controls movement and adjusts operations of an aerial drone. A drone camera observes an aerial maneuver physical gesture by a user. The aerial drone then performs an aerial maneuver that correlates to the aerial maneuver physical gesture. The drone camera observes the user performing a physical action. One or more processors associate the physical action with a particular type of activity. A drone on-board computer adjusts an operation of an aerial drone based on the particular type of activity.

Abstract: An autonomous drone service system controls at least one drone vehicle configured to autonomously navigate along a flight path to provide one or more services requested by a user. The system includes an electronic service provider device to receive at least one service request signal generated by a user device. The request signal indicates at least one requested service provided by the drone service system and location or locations associated with the requested services. The electronic service provider device that automatically maps the at least one requested service to the at least one drone vehicle, and commands the at least one drone vehicle to perform the service request at the one or more locations.

Abstract: An autonomous drone service system controls at least one drone vehicle configured to autonomously navigate along a flight path to provide one or more services requested by a user. The system includes an electronic service provider device to receive at least one service request signal generated by a user device. The request signal indicates at least one requested service provided by the drone service system and location or locations associated with the requested services. The electronic service provider device that automatically maps the at least one requested service to the at least one drone vehicle, and commands the at least one drone vehicle to perform the service request at the one or more locations.

Abstract: A hearing aid method, system, and non-transitory computer readable medium, include a cognitive state analysis circuit configured to analyze a cognitive state of a user, a context analysis circuit configured to analyze a context of the user, and an audio characteristic controller configures to control an audio characteristic of a hearing aid based on a joint assessment of the cognitive state of the user and the context of the user.

Abstract: A processor-implemented method and/or computer program product selectively blocks a self-driving vehicle's access to a roadway. A vehicle interrogation hardware device receives an autonomous capability signal from an approaching self-driving vehicle. One or more processors compare the predefined roadway conditions to current roadway conditions of the access-controlled roadway. In response to the predefined roadway conditions matching the current roadway conditions of the access-controlled roadway within a predetermined range, the processor(s) determine whether the level of autonomous capability of the approaching self-driving vehicle is adequate to safely maneuver the approaching self-driving vehicle through the current roadway conditions of the access-controlled roadway.