Abstract: Handling unacceptable behavior by a participant in a video conference includes detecting the unacceptable behavior by the participant by applying machine learning to data about the participant received from one or more capturing devices and by using a predetermined list of bad habits, determining recognition accuracy for the unacceptable behavior, and providing a response to the unacceptable behavior that varies according to the recognition accuracy. The machine learning may include an initial training phase that, prior to deployment, is used to obtain a general recognition capability for each item on the predetermined list of bad habits. The one or more capturing devices may include a laptop with a camera and a microphone, a mobile device, autonomous cameras, add-on cameras, headsets, regular speakers, smart watches, wristbands, smart rings, and wearable sensors, smart eyewear, heads-up displays, headbands, and/or smart footwear.

Abstract: Constructing a noise pollution map for an area includes a first subset of users performing initial noise recordings in the area using audio devices, using machine learning to provide classification of noises in the initial noise recordings, a second subset of users, larger than the first subset of users, capturing noise in the area using audio devices, creating summaries of noises using the classification to classify noises captured by the second subset of users, and aggregating the summaries to construct the noise pollution map of the area. The audio devices may include headsets, smart speakers, smart television sets, and/or computers. The summaries of noises may be created using software that is installed locally on devices of the second subset of users. The summaries may include source information, amplitude and frequency characteristics, duration, parameters of a corresponding one of the audio devices, user location, surroundings, and/or user movement information.

Abstract: Managing push notifications for a user includes buffering a plurality of notifications, sorting the notifications based on relevance of the notifications to the user and workflow of the user, and delivering the notifications to the user in an order corresponding to sorting the notifications. Sorting may include classifying the notifications into categories that include user notifications, transactional notifications, promotional notifications, and system notifications. Sorting may include associating each of the notifications with aspects of the workflow of the user. Sorting may include scoring notifications according to notification relevance factors and prioritizing the notifications according to an aggregate relevance score of each of the notifications. The notification relevance factors may depend on notification properties, activities of the user, user settings, a physiological and emotional state of the user, and/or user interaction with content.

Abstract: Constructing a noise pollution map for an area includes a first subset of users performing initial noise recordings in the area using audio devices, using machine learning to provide classification of noises in the initial noise recordings, a second subset of users, larger than the first subset of users, capturing noise in the area using audio devices, creating summaries of noises using the classification to classify noises captured by the second subset of users, and aggregating the summaries to construct the noise pollution map of the area. The audio devices may include headsets, smart speakers, smart television sets, and/or computers. The summaries of noises may be created using software that is installed locally on devices of the second subset of users. The summaries may include source information, amplitude and frequency characteristics, duration, parameters of a corresponding one of the audio devices, user location, surroundings, and/or user movement information.