Automatic Generation of Push Notification Alerts of Approaching Emergency Vehicles

In congested cities, it can be difficult for an ambulance or other emergency vehicle to reach a location from which the vehicle is requested. Delay in the arrival of such vehicles is a waste of crucial time due to the emergency nature of the task for such vehicles. This disclosure describes techniques to address the above deficiencies through an alert notification system in a navigation or maps application (app). Vehicles along the route of the emergency vehicle are identified automatically, e.g., based on location information shared from such vehicles via their navigation app. Appropriate notifications are generated and sent to such vehicles. The notifications can include information that an emergency vehicle is approaching and optionally, a suggested action for the vehicle. Vehicles that cannot directly receive push notifications can be notified through vehicle-to-vehicle communication or via on-board sensors

Real-time Contextual Searches to Assist Speakers

Persons who speak professionally to audiences, e.g., teachers, typically spend substantial time and effort to prepare presentation or lecture materials. Answers to follow-up questions from the audience can become more meaningful if the speaker has access to relevant backup material, which, however, may not always be on hand. This disclosure describes techniques to assist speakers in a dynamic, real-time, and automatic manner. Speaker presentation materials and spoken statements are obtained with permission and are used to formulate contextual queries. Results of the queries that include contextually relevant content are grouped by topic and are displayed in a user interface, e.g., as a sidebar that is viewable by the speaker. The results can be displayed in a tabbed user interface that separates content based on content type

Spatially and Temporally Directed Noise Cancellation Using Federated Learning

Machine learning models can be trained to cancel noise of diverse types or spectral characteristics, e.g. traffic noise, background chatter, etc. Such models are trained by feeding training data that includes labeled noise waveforms, which is an expensive and time-consuming procedure. Further, the effectiveness of such machine learning models is limited in canceling types of noise absent from training data. Trained models occupy significant amounts of memory which limits their use in consumer devices. This disclosure describes the use of federated learning techniques to train noise canceling models locally at diverse device locations and times. With user permission, the trained models are tagged with timestamp and location, such that when a user device has time or location matching a particular noise cancellation model, the particular model is provided to the user device. Noise cancellation on the user device is then performed with a compact machine learning model that is suited to the time and location of the user device