Abstract: A method for low-latency communication from a first device to a second device over an unreliable network using at least one predictive machine learning model, characterized in that the method includes: representing at least one frame of time series data at the first device, wherein the at least one frame of time series data is a series of data points indexed in time order; recording at least one output stream, a metadata associated with the at least one output stream, and a plurality of external inputs from the first device in an interaction recorder of the second device, wherein the at least one output stream includes the at least one frame of time series data; segmenting a background area of an image into at least one background area stream, wherein the at least one background area stream is captured from a plurality of users; compressing at least one character centered portion of the image into a character focus stream for enabling an output image to be treated as two streams; training the at least one pre

Abstract: A method for low-latency communication from a first device to a second device over an unreliable network using at least one predictive machine learning model includes representing at least one frame of time series data at the first device; recording at least one output stream, and a plurality of external inputs from the first device in an interaction recorder of the second device detecting, at the second device, at least one lost frame of time series data; training the at least one predictive machine learning model at the first device for predictive frame regeneration; regenerating the at least one lost frame of the time series data at the second device using the at least one predictive machine learning model; and combining an output stream from an application steam with the at least one regenerated frame of time series data to obtain a modified output stream.