Patent ID: 7203635

Claim:
A learning system for signal processing comprising a computer processor and memory for executing the following components, the system is recorded on a computer-readable medium and capable of execution by a computer, comprising the: N classification layers, each of the N classification layers associated with M N probabilistic models per layer, N and M being integers, respectively; at least one parameter defined per a respective classification layer, the parameters trained independently at different classification layers; at least one input stream that is analyzed by the N classification layers, at least one of the input streams sampled according to varying levels of temporal granularity at the respective classification layers; a plurality of time inputs that are applied as sample inputs to the respective layers, the plurality of time inputs are arranged in a descending order of temporal granularity, the descending order refers to lower levels of the N classification layers being sampled at finer time granularities than higher levels of the N classification layers; wherein the N classification layers analyze multiple input streams to determine at least one state associated with a human characteristic, the multiple input streams include at least one of audio data, video data, computer activity data, and other contextual activity data, and wherein at least one of the determined states is stored to enable a computer event; and at least one computer event that is enabled from at least one of the state, and at least one of the computer events is at least one of anticipated, altered, tuned, and adjusted in accordance with a determined state.