Neural network method and apparatus for retrieving signals embedded in noise and analyzing the retrieved signals

Claim 1

An apparatus for retrieving signals embedded in noise and analyzing the signals, comprising an input device for receiving a plurality of input signals comprising noise; noise filtering means, including at least one noise filter, for retrieving data signals embedded in the plurality of input signals received from said input device; first pattern recognition means, including at least one adaptive pattern recognition filter, for generating first coefficients of a polynomial expansion representing the data signals received from said noise filtering means; first storage means for storing the first coefficients generated by said first pattern recognition means; event identifying means for calculating a predicted value of a next data signal based on the first coefficients stored in said first storage means, for comparing a next data signal to the predicted value to determine when an event has occurred, said event located at any position within the data signals, and for ascertaining an exact moment of the event; second pattern recognition means, including an adaptive autoregressive moving average pattern recognition filter, for generating second coefficients of a polynomial expansion representing the data signals received from said noise filtering means; second storage means for storing a plurality of predefined stored patterns each representing a known event type and having a known event location; weighting means, including at least one weighting filter, for retrieving the plurality of predefined stored patterns from said second storage means, aligning the known event location of each of the predefined stored patterns with the exact moment of the even of the data signals and comparing the plurality of predefined stored patterns with the second coefficients generated by the second pattern recognition means, and for generating weights for each of the plurality of stored patterns based on said comparison; and an output device for outputting output signals representing the weights generated by said weighting means.

Claim 2

An apparatus for retrieving signals embedded in noise and analyzing the signals, comprising an input device for receiving a plurality of input signals comprising noise; noise filtering means, including at least one noise filter, for retrieving data signals embedded in the plurality of input signals received from said input device; first pattern recognition means, including at least one adaptive pattern recognition filter, for generating first coefficients of a polynomial expansion representing the data signals received from said noise filtering means; first storage means for storing the first coefficients generated by said first pattern recognition means; event identifying means for calculating a predicted value of a next data signal based on the first coefficients stored in said first storage means, for comparing a next data signal to the predicted value to determine when an event has occurred, said event located at any position within the data signals, and for ascertaining an exact moment of the event; second pattern recognition means, including an adaptive autoregressive moving average pattern recognition filter, for generating second coefficients of a polynomial expansion representing the data signals received from said noise filtering means; second storage means for storing a plurality of predefined stored patterns each representing a known event type and having a known event location; weighting means, including at least one weighting filter, for retrieving the plurality of predefined stored patterns from said second storage means, aligning the known event location of each of the predefined stored patterns with the exact moment of the even of the data signals and comparing the plurality of predefined stored patterns with the second coefficients generated by the second pattern recognition means, and for generating weights for each of the plurality of stored patterns based on said comparison; and an output device for outputting output signals representing the weights generated by said weighting means.

Claim 11

A method for retrieving signals embedded in noise and analyzing the signals comprising the steps of: inputting a plurality of signals comprising noise; filtering said plurality of input signals to retrieve a plurality of filtered data signals from the input signals; passing said plurality of filtered data signals through at least one adaptive pattern recognition filter to generate a first set of coefficients of a polynomial expansion representing the plurality of filtered data signals; storing the first set of coefficients; calculating a predicted value of a next signal based on the first set of coefficients; comparing a next data signal to the predicted value to determine when an event has occurred; ascertaining an exact moment of the event for the plurality of filtered data signals; passing said plurality of filtered data signals through at least one adaptive autoregressive moving average pattern recognition filter to generate a second set of coefficients of a polynomial expansion representing said plurality of filtered data signals; retrieving a plurality of stored patterns from a storage device, each one of the plurality of stored patterns representing a known event type and having a known event location; aligning the known event location of each of the plurality of stored patterns with the exact moment of the event of the plurality of filtered data signals; comparing each one of the plurality of stored patterns with the second set of coefficients to generate weights for each of the plurality of stored patterns; and outputting signals representing the generated weights.

Claim 12

A method for retrieving signals embedded in noise and analyzing the signals comprising the steps of: inputting a plurality of signals comprising noise; filtering said plurality of input signals to retrieve a plurality of filtered data signals from the input signals; passing said plurality of filtered data signals through at least one adaptive pattern recognition filter to generate a first set of coefficients of a polynomial expansion representing the plurality of filtered data signals; storing the first set of coefficients; calculating a predicted value of a next signal based on the first set of coefficients; comparing a next data signal to the predicted value to determine when an event has occurred; ascertaining an exact moment of the event for the plurality of filtered data signals; passing said plurality of filtered data signals through at least one adaptive autoregressive moving average pattern recognition filter to generate a second set of coefficients of a polynomial expansion representing said plurality of filtered data signals; retrieving a plurality of stored patterns from a storage device, each one of the plurality of stored patterns representing a known event type and having a known event location; aligning the known event location of each of the plurality of stored patterns with the exact moment of the event of the plurality of filtered data signals; comparing each one of the plurality of stored patterns with the second set of coefficients to generate weights for each of the plurality of stored patterns; and outputting signals representing the generated weights.

Claim 20

The method of any of claims 10, 15, 16, or 17 further comprising the step of: outputting signals representing the generated coefficients of the polynomial expansion representing the pattern of data signals.

Claim 24

The method of any of claims 11, 16, 17, 17, 20, or 21 wherein the plurality of signals comprising noise represent prices of stock as a function of time.

Claim 25

An apparatus for retrieving signals embedded in noise and analyzing the signals, comprising an input device for receiving a plurality of input signals comprising noise; noise filtering means for retrieving a plurality of data signals embedded in the plurality of input signals received from said input device; pattern recognition means for generating coefficients of a polynomial expansion representing the plurality of data signals received from said noise filtering means; first storage means for storing the coefficients generated by the pattern recognition means; event identifying means if or calculating a predicted value of a next data signal based on the coefficients stored in said first storage means, for comparing a next data signal to the predicted value to determine when an event has occurred, said event located at any position within the data signals, and for ascertaining an exact moment of the event; second storage means for storing a plurality of predefined stored patterns each representing a known event type and having a known event location; weighting means for retrieving the plurality of predefined stored patterns from said second storage means, aligning the known event location of each of the predefined stored patterns with the exact moment of the event of the plurality of data signals and comparing the plurality of predefined stored patterns with the coefficients generated by the pattern recognition means, and for generating weights for each of the plurality of stored patterns based on said comparison; and an output device for outputting signals representing the weights generated by said weighting means.

Claim 26

An apparatus for retrieving signals embedded in noise and analyzing the signals, comprising an input device for receiving a plurality of input signals comprising noise; noise filtering means for retrieving a plurality of data signals embedded in the plurality of input signals received from said input device; pattern recognition means for generating coefficients of a polynomial expansion representing the plurality of data signals received from said noise filtering means; first storage means for storing the coefficients generated by the pattern recognition means; event identifying means if or calculating a predicted value of a next data signal based on the coefficients stored in said first storage means, for comparing a next data signal to the predicted value to determine when an event has occurred, said event located at any position within the data signals, and for ascertaining an exact moment of the event; second storage means for storing a plurality of predefined stored patterns each representing a known event type and having a known event location; weighting means for retrieving the plurality of predefined stored patterns from said second storage means, aligning the known event location of each of the predefined stored patterns with the exact moment of the event of the plurality of data signals and comparing the plurality of predefined stored patterns with the coefficients generated by the pattern recognition means, and for generating weights for each of the plurality of stored patterns based on said comparison; and an output device for outputting signals representing the weights generated by said weighting means.

Claim 30

An apparatus for retrieving signals embedded in noise and analyzing the signals, comprising an input device for receiving a plurality of input signals comprising noise; a noise filter for retrieving a plurality of data signals embedded in the plurality of input signals received from the input device; at least one pattern recognition filter for generating coefficients of a polynomial expansion representing the plurality of data signals received from the noise filter; a storage device for storing the coefficients generated by the pattern recognition filter and for storing a plurality of predefined stored patterns each representing a known event type and having a known event location; means for calculating a predicted value of a data signal based on the coefficients; means for comparing at last one of the plurality of data signals to the predicted value to determine when an event has occurred, said event located at any position within the data signals; means for ascertaining an exact moment of the event; means for retrieving the plurality of predefined stored patterns from the storage device; means for aligning the known event location of each of the predefined stored patterns with the exact moment of the event of the plurality of data signals; means for comparing the plurality of predefined stored patterns with the coefficients and generating weights for each of the plurality of stored patterns; and an output device for outputting signals representing the weights generated.

Claim 31

An apparatus for retrieving signals embedded in noise and analyzing the signals, comprising an input device for receiving a plurality of input signals comprising noise; a noise filter for retrieving a plurality of data signals embedded in the plurality of input signals received from the input device; at least one pattern recognition filter for generating coefficients of a polynomial expansion representing the plurality of data signals received from the noise filter; a storage device for storing the coefficients generated by the pattern recognition filter and for storing a plurality of predefined stored patterns each representing a known event type and having a known event location; means for calculating a predicted value of a data signal based on the coefficients; means for comparing at last one of the plurality of data signals to the predicted value to determine when an event has occurred, said event located at any position within the data signals; means for ascertaining an exact moment of the event; means for retrieving the plurality of predefined stored patterns from the storage device; means for aligning the known event location of each of the predefined stored patterns with the exact moment of the event of the plurality of data signals; means for comparing the plurality of predefined stored patterns with the coefficients and generating weights for each of the plurality of stored patterns; and an output device for outputting signals representing the weights generated.

Claim 35

An method for retrieving signals embedded in noise and analyzing the signals, comprising the steps of: receiving a plurality of input signals comprising noise; filtering the plurality of input signals to obtain a plurality of data signals; filtering the plurality of data signals to generate coefficients of a polynomial expansion representing the plurality of data signals; calculating a predicted value of a data signal based on the coefficients; comparing at least one of the plurality a data signals to the predicted value to determine when an event has occurred, said event located at any position within the data signals; ascertaining an exact moment of the event; providing a plurality of predefined stored patterns each representing a known event type and having a known event location; aligning the known event location of each of the predefined stored patterns with the exact moment of the event of the plurality of data signals; comparing the plurality of predefined stored patterns with the coefficients and generating weights for each of the plurality of stored patterns; and outputting signals representing the weights generated.

Claim 36

An method for retrieving signals embedded in noise and analyzing the signals, comprising the steps of: receiving a plurality of input signals comprising noise; filtering the plurality of input signals to obtain a plurality of data signals; filtering the plurality of data signals to generate coefficients of a polynomial expansion representing the plurality of data signals; calculating a predicted value of a data signal based on the coefficients; comparing at least one of the plurality a data signals to the predicted value to determine when an event has occurred, said event located at any position within the data signals; ascertaining an exact moment of the event; providing a plurality of predefined stored patterns each representing a known event type and having a known event location; aligning the known event location of each of the predefined stored patterns with the exact moment of the event of the plurality of data signals; comparing the plurality of predefined stored patterns with the coefficients and generating weights for each of the plurality of stored patterns; and outputting signals representing the weights generated.