Abstract: An ensemble of machine learning models used for real-time prediction of text for an electronic chat with an expert user. A global machine learning model, e.g., a transformer model, trained with domain specific knowledge makes a domain specific generalized prediction. Another machine learning model, e.g., an n-gram model, learns the specific style of the expert user as the expert user types to generate more natural, more expert user specific text. If specific words cannot be predicted with a desired probability level, another word level machine learning model, e.g., a word completion model, completes the words as the characters are being typed. The ensemble therefore produces real-time, natural, and accurate text that is provided to the expert user. Continuous feedback of the acceptance/rejection of predictions by the expert is used to fine tune one or more machine learning models of the ensemble in real time.

Abstract: An ensemble of machine learning models used for real-time prediction of text for an electronic chat with an expert user. A global machine learning model, e.g., a transformer model, trained with domain specific knowledge makes a domain specific generalized prediction. Another machine learning model, e.g., an n-gram model, learns the specific style of the expert user as the expert user types to generate more natural, more expert user specific text. If specific words cannot be predicted with a desired probability level, another word level machine learning model, e.g., a word completion model, completes the words as the characters are being typed. The ensemble therefore produces real-time, natural, and accurate text that is provided to the expert user. Continuous feedback of the acceptance/rejection of predictions by the expert is used to fine tune one or more machine learning models of the ensemble in real time.

Abstract: Embodiments herein provide a method for providing personalized response suggestions to a user for a notification event using an electronic device. The method includes detecting the notification event associated with a user. The method includes authenticating presence of the user for the notification event based on Ultra-Wide Band (UWB) signal data obtained from the electronic device and/or one or more IoT devices. The method includes determining current activity of the user based on the UWB signal data in response to a successful authentication. The method includes correlating the current activity with a plurality of activities performed by the user in past, and a past interaction pattern of the user in connection with events substantially similar to nature of the notification event. The method includes generating one or more auto response suggestions for the notification event as a result of correlation.