Abstract: Context-dependent memory stimulation is provided. A context-dependent task resumption memory cue personalized to a user is sent to a client device of the user via a network during a task interruption lag period corresponding to a primary task prior to the user starting a set of secondary tasks to support a context-dependent memory of the user associated with the primary task. It is determined that the user started and then stopped performing the set of secondary tasks based on monitoring the client device. The context-dependent task resumption memory cue personalized to the user is resent to the client device via the network during a task resumption lag period corresponding to the primary task to stimulate the context-dependent memory of the user associated with the primary task in response to the computer determining that the user stopped performing the set of secondary tasks and resumed the primary task.

Abstract: A plurality of key features that contribute to a level of anomalousness of an anomalous subgroup are identified. One or more minimal perturbations to a set of features of the anomalous subgroup that result in a reduction of the level of anomalousness are identified. The application of one or more minimal perturbations to members of the anomalous subgroup is facilitated.

Abstract: An embodiment associates a virtual-meeting host designation with a first user. During the virtual meeting, the embodiment generates meeting context data that includes user characteristic data and meeting characteristic data. The embodiment uses the meeting context data to generate a predicted end time for the virtual meeting and predicts a meeting run-over condition indicative of the virtual meeting continuing past a scheduled end time by comparing the predicted end time and a scheduled end time. Responsive to predicting the meeting run-over condition, the embodiment uses the user characteristic data to generate a ranked list of other users and renders, as a display to the first user, the ranked list and an indication of the meeting run-over condition. The embodiment receives a selection of a second user as a new host of the virtual meeting and, responsive to receiving the selection, associates the host designation with the second.

Abstract: A method for regulating emotions in conversations is disclosed. In one embodiment, such a method includes monitoring a conversation between participants. The method further divides the conversation into a plurality of utterances and calculates an emotion score for each utterance. The method further determines whether an emotion score of an utterance exceeds a threshold. In the event the emotion score exceeds the threshold, the method intervenes in the conversation in an attempt to return the conversation to a more constructive path. In certain embodiments, this intervention may include taking one or more actions that are specifically tailored to returning the conversation to a more constructive path. In other or the same embodiments, responses or reactions of the participants to the actions are audited to determine if the actions were successful in returning the conversation to a more constructive path. A corresponding system and computer program product are also disclosed.

Abstract: Methods, systems, and computer program products for predicting states from patient-caregiver dyadic biomarker data using artificial intelligence are provided herein. A computer-implemented method includes obtaining biomarker data derived from one or more dyads, each dyad comprising at least one patient and at least one caregiver associated with the at least one patient; determining, based on processing the obtained biomarker data, data-based representations of mental distress and/or social rhythm disruption among at least one of the one or more dyads; predicting mental distress and/or social rhythm disruption among a given dyad of at least one patient and at least one caregiver associated with the at least one patient by processing input biomarker data, derived from the given dyad, using artificial intelligence techniques in connection with at least a portion of the data-based representations; and performing automated actions based on the predicting.