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 computer-implemented method, system, and/or computer program product controls self-driving vehicles (SDVs). An emergency message is transmitted to a receiver within a self-driving vehicle (SDV). The emergency message describes an emergency state of an emergency vehicle and an identified future route of the emergency vehicle. In response to the SDV receiving the emergency message, the SDV is redirected, via an auto-control hardware system on the SDV, to a location and on a route that does not conflict with the identified future route of the emergency vehicle.

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 processor-implemented method, system, and/or computer program product control a driving mode of a self-driving vehicle (SDV). One or more processors detect that an SDV is being operated in manual mode by a human driver. The processor(s) determine that the human driver is unqualified to operate the SDV in manual mode, and then transfer control of the SDV to an SDV on-board computer in order to place the SDV in autonomous mode.

Abstract: A method of visualizing and interacting with a given term graph for a given group G defined by a set of values d for a set of dimensions D and topic X, may comprise obtaining the term graph associated with the given group G and the topic X; displaying the topic X in a tag cloud; representing each term from the term graph as a tag in the tag cloud, wherein a distance of a given tag from the displayed topic X in the tag cloud represents a distance of a term represented by the given tag from the topic X in the term graph; and visually representing a tag's importance relative to one or more of resources associated with the given group G in the tag cloud.

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 of visualizing and interacting with a given term graph for a given group G defined by a set of values d for a set of dimensions D and topic X, may comprise obtaining the term graph associated with the given group G and the topic X; displaying the topic X in a tag cloud; representing each term from the term graph as a tag in the tag cloud, wherein a distance of a given tag from the displayed topic X in the tag cloud represents a distance of a term represented by the given tag from the topic X in the term graph; and visually representing a tag's importance relative to one or more of resources associated with the given group G in the tag cloud.

Abstract: A method, system, and/or computer program product controls a self-driving vehicle based on a purchase transaction at a point of sale (POS) device. One or more processors detect a purchase transaction at the POS device. In response to detecting the purchase transaction at the POS device, the processor(s) transmit instructions to the SDV to pick up a passenger associated with the purchase transaction at a predetermined location.

Abstract: A cognitive mirror apparatus includes an imaging device configured to capture a plurality of images, a controller configured to determine a cognitive state of a user; and a cognitive mirror configured to display a virtual reflection comprising one or more of the plurality of images, wherein the virtual reflection of the cognitive mirror is determined using the cognitive state of a user.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). Sensor readings describe a current condition of a roadway, which is part of a planned route of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of the on-board SDV control processor that autonomously controls the SDV to a human driver competence level of a human driver in controlling the SDV under the current condition of the roadway. One or more processors then selectively assign control of the SDV to the on-board SDV control processor or to the human driver based on which of the control processor competence level and the human driver competence level is relatively higher to the other.

Abstract: A method, system, and/or computer program product pre-positions an aerial drone for a user. A model of a user is used as a basis for predicting a future task to be performed by the user at a future time and at a particular location. One or more processors identify sensor data that will be required by the user in order to perform the future task at the future time and at the particular location, where the sensor data is generated by one or more sensors on the aerial drone. A transmitter then transmits a signal to the aerial drone to pre-position the aerial drone at the particular location before the future time.

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 method, system, and/or computer program product controls operations of an aerial drone within a predetermined airspace. A drone controller device detects a presence of an aerial drone. The drone controller device and the aerial drone negotiate permission to fly within a predetermined airspace under a predefined aerial drone state. In response to successfully negotiating the permission, the drone controller device enables a drone on-board computer to operate the aerial drone within the predetermined airspace in accordance with the predefined aerial drone state.

Abstract: A cognitive mirror apparatus includes an imaging device configured to capture a plurality of images, a controller configured to determine a cognitive state of a user; and a cognitive mirror configured to display a virtual reflection comprising one or more of the plurality of images, wherein the virtual reflection of the cognitive mirror is determined using the cognitive state of a user.

Abstract: A method, system, and/or computer program product pre-positions an aerial drone for a user. A model of a user is used as a basis for predicting a future task to be performed by the user at a future time and at a particular location. One or more processors identify sensor data that will be required by the user in order to perform the future task at the future time and at the particular location, where the sensor data is generated by one or more sensors on the aerial drone. A transmitter then transmits a signal to the aerial drone to pre-position the aerial drone at the particular location before the future time.

Abstract: A method, and associated system and computer program product, for managing braking of a moving vehicle. A speed and deceleration of the moving vehicle are monitored. External and/or internal conditions relating to the moving vehicle and/or the vehicle's driver are detected. A hardware processor: calculates a braking distance for the moving vehicle for the detected external and/or internal conditions from the monitored speed and deceleration of the moving vehicle; determines a spacing distance between the moving vehicle and a second vehicle ahead of the moving vehicle; adds a cascading response distance to the calculated braking distance for a third vehicle ahead of the second vehicle; and determines that the calculated braking distance is greater than the determined spacing distance. An output is generated in response to the determination that the calculated braking distance is greater than the obtained spacing distance.

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: A method and/or computer program product controls a physical spacing between self-driving vehicles (SDVs). One or more processors receive a social network node graph. The social network node graph describes a graphical distance between a first node on the social network node graph and a second node on the social network node graph. The first node represents a first passenger in a first SDV; the second node represents a second passenger in a second SDV; and the graphical distance between the first node and the second node describes a relationship level in a social network between the first passenger and the second passenger. An SDV on-board computer on at least one of the first SDV and the second SDV adjusts a physical spacing between the first SDV and the second SDV proportional to the graphical distance between the first node and the second node.

Abstract: An unmanned aerial vehicle for determining geolocation exclusion zones of animals. The unmanned aerial vehicle includes a processor-based monitoring device to track geolocation information associated with an animal from the unmanned aerial vehicle, an identification device mounted on the unmanned aerial vehicle to identify the animal and to track a position of the animal over time, and a mapping device coupled to the monitoring device to determine locations where the animal has traversed and to identify where an encounter with the animal is reduced.

Abstract: Detecting user input based on multiple gestures is provided. One or more interactions are received from a user via a user interface. An inferred interaction is determined based, at least in part, on a geometric operation, wherein the geometric operation is based on the one or more interactions. The inferred interaction is presented via the user interface. Whether a confirmation has been received for the inferred interaction is determined.