Abstract: An aerial drone includes a pest sensor, an environmental sensor, a drone on-board computer, and a pest abatement mechanism. The pest sensor senses a pest based on emissions from the pest. The environmental sensor detects an environment of the pest. The drone on-board computer identifies a pest type of the pest based on the emission from the pest, and establishes a risk level posed by the presence of the pest based on the pest type and the environment of the pest. The pest abatement mechanism performs a pest abatement of the pest based on the pest type and the risk level posed by the presence of the pest.

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: A computer-implemented method, system, and/or computer program product redirects a self-driving vehicle (SDV) to a physical location of a product provider. A self-driving vehicle (SDV) on-board computer on an SDV receives a current location and a planned route to an initial destination of the SDV. The SDV on-board computer receives information describing real-time physiological states of one or more occupants of the SDV, and then identifies a physical location of a product provider that provides a solution to ameliorate the real-time physiological states of the one or more occupants of the SDV. The SDV on-board computer then alters the planned route of the SDV to redirect the SDV on-board computer to autonomously drive the SDV to the physical location of the product provider.

Abstract: Embodiments include method, systems and computer program products for route planning and management with a drone. Aspects include receiving a destination for an individual and determining multiple routes between a position of the individual and the destination. Aspects further include deploying the drone to determine safety and accessibility risks associated with the multiple routes and determining a preferred route from the multiple routes based on the safety and accessibility risks associated with the multiple routes.

Abstract: A computer-implemented method, system, and/or computer program product enables automatic toll booth interaction with self-driving vehicles (SDVs). An SDV interrogation transceiver at a toll booth interrogates a driving mode module on an SDV. The SDV is capable of being operated in autonomous mode by an on-board SDV control processor. The driving mode module selectively controls the SDV to be operated in the autonomous mode or in manual mode, in which a human driver of the SDV manually operates the SDV. The SDV interrogation transceiver receives a driving mode descriptor of the SDV, which identifies whether the SDV currently is operating in the autonomous mode or in the manual mode while traveling on a toll road. An adjusted toll charge for the SDV to travel on the toll road is then transmitted based on the driving mode descriptor.

Abstract: A computer-implemented method, system, and/or computer program product automatically provide spatial separation between self-driving vehicles (SDVs) operating in an autonomous mode and vehicles being operating in a manual mode on a roadway. A first SDV operating on the roadway is operating in autonomous mode. A second vehicle may be operating in the autonomous mode or a manual mode, in which a driver is controlling the second vehicle. Processor(s) issue spatial separation instructions to the first SDV, which direct SDV control mechanisms controller on the first SDV to direct a set of SDV vehicular physical control mechanisms on the first SDV to provide a predetermined amount of spatial separation between the first SDV and the second vehicle, based on whether the second vehicle is being operated in the manual mode or in the autonomous mode.

Abstract: A processor-implemented method selectively controls 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 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.

Abstract: A method and associated systems for using methods of DNA computing to implement an operation of a natural-language processing (NLP) system. A processor translates components of a slot grammar of the NLP system and an input filler vocabulary into listings of sequences of nucleotides. These sequences are encoded into a set of nucleotide chains, which are then allowed to chemically interact with each other such that the chains automatically bond in ways that are analogous to the way that a traditional computerized NLP system would use inferences and contextual information to fill slots of the slot grammar with tokens of the filler vocabulary. The resulting DNA molecules are extracted and information encoded into sequences of nucleotides comprised by the extracted molecules is decoded to yield a set of all possible strings that may generated by filling slots of the slot grammar with tokens of the vocabulary.

Abstract: A method of providing a user interface with recipient status information, in one aspect, may comprise detecting a message (e.g., online message such as instant messaging, chat, etc.) being initiated by a first user to a second user; gathering information associated with the second user; analyzing the gathered information; predicting a state of the second user based on the analyzing; and determining a notification action based on the predicted state of the second user, the notification action notifying the first user of the second user's state; and presenting a notification comprising one or more of graphical, textual, auditory, or tactile indications or combinations thereof to the first user.

Abstract: A method of providing a user interface with recipient status information, in one aspect, may comprise detecting a message (e.g., online message such as instant messaging, chat, etc.) being initiated by a first user to a second user; gathering information associated with the second user; analyzing the gathered information; predicting a state of the second user based on the analyzing; and determining a notification action based on the predicted state of the second user, the notification action notifying the first user of the second user's state; and presenting a notification comprising one or more of graphical, textual, auditory, or tactile indications or combinations thereof to the first user.

Abstract: Embodiments include method, systems and computer program products for route planning and management with a drone. Aspects include receiving a destination for an individual and determining multiple routes between a position of the individual and the destination. Aspects further include deploying the drone to determine safety and accessibility risks associated with the multiple routes and determining a preferred route from the multiple routes based on the safety and accessibility risks associated with the multiple routes.

Abstract: An aspect of applying tacit knowledge to iteratively refine datasets includes determining, via a computer processor, that a data element in the dataset is potentially in non-conformance with other data in the dataset. The potential non-conformance is determined based on a discrepancy in a pattern noted in the dataset with respect to the data element. The dataset spans multiple knowledge domains. An aspect also includes annotating a data structure containing the data element to indicate the potential non-conformance and providing, via a user interface of the computer processor, a plurality of users with access to the data structure. The users collectively indicate domain experts for each of the multiple knowledge domains.

Abstract: A self-driving vehicle (SDV) ameliorates a vehicular mishap incurred by a second vehicle. At least one sensor on a first SDV detects a second vehicle that has been involved in a vehicular mishap. One or more processors determine a confidence level L, which is a confidence level of a mishap assessment accuracy of determining that the second vehicle has been involved in the vehicular mishap. In response to the confidence level L exceeding a predetermined value, the SDV executes an amelioration action to ameliorate a condition of the second vehicle that has been involved in the vehicular mishap.

Abstract: Preventing unintended distribution of audio information may comprise analyzing audio data of a speaker's speech received by a microphone; determining automatically by a processor, from the analyzing whether the speaker's speech is intended to be distributed to an audience via the microphone; and in response to determining that the speaker's speech is not intended to be distributed to the audience via the microphone, performing one or more actions.

Abstract: Embodiments include method, systems and computer program products for route planning and management with a drone. Aspects include receiving a destination for an individual and determining multiple routes between a position of the individual and the destination. Aspects further include deploying the drone to determine safety and accessibility risks associated with the multiple routes and determining a preferred route from the multiple routes based on the safety and accessibility risks associated with the multiple routes.

Abstract: An embodiment of the invention may include a method, a computer program product and a computer system for assessing interactions towards an electronic device. The embodiment may include a computing device that monitors a pattern of actions of a first user, where the first user is associated with a first electronic device. The embodiment may include a computing device that determines that at least one action from the first user indicates the first user is undergoing an aggressive act. The embodiment may include a computing device that responds to the aggressive act by: communicating results of the determination that the first pattern matches the data pattern to a second electronic device; and/or sending information detailing a command to activate a device component of one or both of the first electronic device and a third electronic device.

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: 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: 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 and/or computer program product controls an operational mode of a self-driving vehicle (SDV) that is initially being operated in a nominal autonomous mode. Detectors on an SDV detect an erratically driven vehicle (EDV) that is being operated in an unsafe manner within a predetermined distance of an SDV. In response to detecting the EDV, a driving mode device in the SDV changes an operational mode of the SDV from the nominal autonomous mode to an evasive autonomous mode.