US5617513A
Expired / Lapsed
US
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Method for analyzing activity in a signal
Assignee: NOISEOUT Inc
•
Filing: 1995-01-04
•
Granted: 1997-04-01
•
Expires: 2015-01-04
Abstract
Abstract A method for analyzing a signal. The signal is passed through a noise filter to generate a filtered signal. The filtered signal is passed through an adaptive pattern recognition unit. At the adaptive pattern recognition unit, a pattern that is a function of the filtered signal is predicted. A mathematical deviation in the filtered signal is detected by comparing the filtered signal with the predicted pattern. The detected mathematical deviation is flagged. The filtered signal is passed through at least two pattern recognition weighing units. At each pattern recognition weighing unit, the filtered signal at about the detected mathematical deviation is matched with a collection of stored patterns. A weight is assigned to each stored pattern. Each stored pattern and the assigned weight of the stored pattern are input to an expert system ranking unit. At the expert system ranking unit, one of the stored patterns is selected by applying a predefined set of generalized rules. Additionally, the method may determine if activity in a signal is individual event activity. A moment of chaos in the signal may also be determined.
Inventors
- 👤 Bonnie S. Schnitta
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-30 19:29
CPC Classifications
G; G01; G01S; G01S7/00; G01S7/02; G01S7/28; G01S7/285; G01S7/292; G01S7/41; G01S7/417; G01V; G01V1/00; G01V1/01; G01V1/28; G01V1/288; H; H03; H03H; H03H21/00; H03H21/0012; G06; G06F; G06F2218/00; G06F2218/02; G06F2218/04; H03H2222/00; H03H2222/04; Y; Y10; Y10S; Y10S706/00; Y10S706/902; Y10S706/925; Y10S706/928; Y10S706/929
Claims Analysis
Total Claims: 43
Claim 1
Independent Claim
162 words
A method for determining if activity in a signal is market activity comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through a non-adaptive noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the filtered signal; determining if there is a first deviation in the filtered signal by comparing the filtered signal with the predicted first pattern; passing the unfiltered signal through the adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the unfiltered signal; determining if there is a second deviation in the unfiltered signal by comparing the unfiltered signal with the predicted second pattern; and if there exists a second deviation in the unfiltered signal and no first deviation in the filtered signal, outputting an indication of market activity.
Claim 2
Independent Claim
162 words
A method for determining if activity in a signal is market activity comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through a non-adaptive noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the filtered signal; determining if there is a first deviation in the filtered signal by comparing the filtered signal with the predicted first pattern; passing the unfiltered signal through the adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the unfiltered signal; determining if there is a second deviation in the unfiltered signal by comparing the unfiltered signal with the predicted second pattern; and if there exists a second deviation in the unfiltered signal and no first deviation in the filtered signal, outputting an indication of market activity.
Claim 3
Dependent Claim
34 words
The method of claim 1 wherein the step of passing the unfiltered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through a self adaptive and variant error ("SAVE") filter.
Claim 4
Dependent Claim
34 words
The method of claim 1 wherein the step of passing the filtered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through a self adaptive and variant error ("SAVE") filter.
Claim 5
Dependent Claim
31 words
The method of claim 1 wherein the step of passing the unfiltered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through an adaptive TRAPS noise filter.
Claim 6
Dependent Claim
31 words
The method of claim 1 wherein the step of passing the filtered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through an adaptive TRAPS noise filter.
Claim 7
Independent Claim
168 words
A method for determining if activity in a signal is individual event activity comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through a non-adaptive noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the filtered signal; determining if there is a first deviation in the filtered signal by comparing the filtered signal with the predicted first pattern; passing the unfiltered signal through the adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the unfiltered signal; determining if there is a second deviation in the unfiltered signal by comparing the unfiltered signal with the predicted second pattern; and if it is determined that there is a first deviation in the filtered signal and no second deviation in the unfiltered signal, outputting an indication of individual event activity.
Claim 8
Independent Claim
168 words
A method for determining if activity in a signal is individual event activity comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through a non-adaptive noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the filtered signal; determining if there is a first deviation in the filtered signal by comparing the filtered signal with the predicted first pattern; passing the unfiltered signal through the adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the unfiltered signal; determining if there is a second deviation in the unfiltered signal by comparing the unfiltered signal with the predicted second pattern; and if it is determined that there is a first deviation in the filtered signal and no second deviation in the unfiltered signal, outputting an indication of individual event activity.
Claim 9
Dependent Claim
28 words
The method of claim 6 wherein the unfiltered signal represents stock prices for a plurality of stocks and the individual event activity represents event activity for one stock.
Claim 10
Independent Claim
162 words
A method for determining if activity in a signal is market activity comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through an adaptive noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the filtered signal; determining if there is a first deviation in the filtered signal by comparing the filtered signal with the predicted first pattern; passing the unfiltered signal through the adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the unfiltered signal; determining if there is a second deviation in the unfiltered signal by comparing the unfiltered signal with the predicted second pattern; and if there exists a second deviation in the unfiltered signal and no first deviation in the filtered signal, outputting an indication of market activity.
Claim 11
Independent Claim
162 words
A method for determining if activity in a signal is market activity comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through an adaptive noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the filtered signal; determining if there is a first deviation in the filtered signal by comparing the filtered signal with the predicted first pattern; passing the unfiltered signal through the adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the unfiltered signal; determining if there is a second deviation in the unfiltered signal by comparing the unfiltered signal with the predicted second pattern; and if there exists a second deviation in the unfiltered signal and no first deviation in the filtered signal, outputting an indication of market activity.
Claim 12
Dependent Claim
34 words
The method of claim 8 wherein the step of passing the unfiltered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through a self adaptive and variant error ("SAVE") filter.
Claim 13
Dependent Claim
34 words
The method of claim 8 wherein the step of passing the filtered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through a self adaptive and variant error ("SAVE") filter.
Claim 14
Dependent Claim
31 words
The method of claim 8 wherein the step of passing the unfiltered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through an adaptive TRAPS noise filter.
Claim 15
Dependent Claim
31 words
The method of claim 8 wherein the step of passing the filtered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through an adaptive TRAPS noise filter.
Claim 16
Independent Claim
168 words
A method for determining if activity in a signal is individual event activity comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through an adaptive noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the filtered signal; determining if there is a first deviation in the filtered signal by comparing the filtered signal with the predicted first pattern; passing the unfiltered signal through the adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the unfiltered signal; determining if there is a second deviation in the unfiltered signal by comparing the unfiltered signal with the predicted second pattern; and if it is determined that there is a first deviation in the filtered signal and no second deviation in the unfiltered signal, outputting an indication of individual event activity.
Claim 17
Independent Claim
168 words
A method for determining if activity in a signal is individual event activity comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through an adaptive noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the filtered signal; determining if there is a first deviation in the filtered signal by comparing the filtered signal with the predicted first pattern; passing the unfiltered signal through the adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the unfiltered signal; determining if there is a second deviation in the unfiltered signal by comparing the unfiltered signal with the predicted second pattern; and if it is determined that there is a first deviation in the filtered signal and no second deviation in the unfiltered signal, outputting an indication of individual event activity.
Claim 18
Dependent Claim
28 words
The method of claim 13 wherein the unfiltered signal represents stock prices for a plurality of stocks and the individual event activity represents event activity for one stock.
Claim 19
Dependent Claim
34 words
The method of claim 13 wherein the step of passing the unfiltered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through a self adaptive and variant error ("SAVE") filter.
Claim 20
Dependent Claim
34 words
The method of claim 13 wherein the step of passing the filtered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through a self adaptive and variant error ("SAVE") filter.
Claim 21
Dependent Claim
31 words
The method of claim 13 wherein the step of passing the unfiltered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through an adaptive TRAPS noise filter.
Claim 22
Dependent Claim
31 words
The method of claim 13 wherein the step of passing the filtered signal through the adaptive pattern recognition unit further comprises passing the unfiltered signal through an adaptive TRAPS noise filter.
Claim 23
Independent Claim
87 words
A method for determining a moment of chaos in a signal comprising the steps of: passing the signal through a noise filter to generate a filtered data signal; passing the filtered data signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a pattern that is a function of the filtered data signal; detecting a mathematical deviation in the filtered data signal by comparing the filtered data signal with the predicted pattern; and flagging the detected mathematical deviation as the moment of chaos.
Claim 24
Independent Claim
87 words
A method for determining a moment of chaos in a signal comprising the steps of: passing the signal through a noise filter to generate a filtered data signal; passing the filtered data signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a pattern that is a function of the filtered data signal; detecting a mathematical deviation in the filtered data signal by comparing the filtered data signal with the predicted pattern; and flagging the detected mathematical deviation as the moment of chaos.
Claim 25
Dependent Claim
14 words
The method of claim 19 wherein the noise filter is a non-adaptive noise filter.
Claim 26
Dependent Claim
15 words
The method of claim 19 wherein the noise filter is an adaptive TRAPS noise filter.
Claim 27
Dependent Claim
36 words
The method of claim 19 wherein the step of passing the filtered data signal through the adaptive pattern recognition unit further comprises passing the filtered data signal through a self adaptive and variant error ("SAVE") filter.
Claim 28
Dependent Claim
33 words
The method of claim 19 wherein the step of passing the filtered data signal through the adaptive pattern recognition unit further comprises passing the filtered data signal through an adaptive TRAPS noise filter.
Claim 29
Independent Claim
193 words
A method for analyzing a signal comprising the steps of: providing an expert system ranking unit; passing the signal through a noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a pattern that is a function of the filtered signal; detecting a mathematical deviation in the filtered signal by comparing the filtered signal with the predicted pattern; flagging the detected mathematical deviation; passing the filtered signal through at least two pattern recognition weighing units, the at least two pattern recognition weighing units comprising a first pattern recognition weighing unit being an entopic differential weighing unit and a second pattern recognition weighing unit being an adaptive pattern recognition weighing unit; at each pattern recognition weighing unit, matching the filtered signal at about the detected mathematical deviation with a collection of stored patterns and assigning a weight to each stored pattern; inputting each stored pattern and the assigned weight of the stored pattern to the expert system ranking unit; and at the expert system ranking unit, selecting one of the stored patterns by applying a predefined set of generalized rules.
Claim 30
Independent Claim
193 words
A method for analyzing a signal comprising the steps of: providing an expert system ranking unit; passing the signal through a noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a pattern that is a function of the filtered signal; detecting a mathematical deviation in the filtered signal by comparing the filtered signal with the predicted pattern; flagging the detected mathematical deviation; passing the filtered signal through at least two pattern recognition weighing units, the at least two pattern recognition weighing units comprising a first pattern recognition weighing unit being an entopic differential weighing unit and a second pattern recognition weighing unit being an adaptive pattern recognition weighing unit; at each pattern recognition weighing unit, matching the filtered signal at about the detected mathematical deviation with a collection of stored patterns and assigning a weight to each stored pattern; inputting each stored pattern and the assigned weight of the stored pattern to the expert system ranking unit; and at the expert system ranking unit, selecting one of the stored patterns by applying a predefined set of generalized rules.
Claim 31
Dependent Claim
14 words
The method of claim 24 wherein the noise filter is a non-adaptive noise filter.
Claim 32
Dependent Claim
15 words
The method of claim 24 wherein the noise filter is an adaptive TRAPS noise filter.
Claim 33
Dependent Claim
34 words
The method of claim 24 wherein the step of passing the filtered signal through the adaptive pattern recognition unit further comprises passing the filtered signal through a self adaptive and variant error ("SAVE") filter.
Claim 34
Dependent Claim
31 words
The method of claim 24 wherein the step of passing the filtered signal through the adaptive pattern recognition unit further comprises passing the filtered signal through an adaptive TRAPS noise filter.
Claim 35
Independent Claim
368 words
A method for analyzing a signal comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through a non-adaptive noise filter to generate a first filtered data signal; passing the unfiltered signal through an adaptive TRAPS filter to generate a second filtered data signal; passing the unfiltered signal, the first filtered data signal and the second filtered data signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the first filtered data signal; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the second filtered data signal; at the adaptive pattern recognition unit, predicting a third pattern that is a function of the unfiltered signal; detecting a first mathematical deviation in the first filtered data signal by comparing the first filtered data signal with the first predicted pattern; detecting a second mathematical deviation in the second filtered data signal by comparing the second filtered data signal with the second predicted pattern; detecting a third mathematical deviation in the unfiltered signal by comparing the unfiltered signal with the third predicted pattern; flagging the first mathematical deviation, the second mathematical derivation and the third mathematical deviation; passing the first filtered data signal, the second filtered data signal and the unfiltered signal through at least two pattern recognition weighing units; at each pattern recognition weighing unit, matching the first filtered data signal at about the detected first mathematical deviation with a collection of stored patterns and assigning a weight to each stored pattern; at each pattern recognition weighing unit, matching the second filtered data signal at about the detected second mathematical deviation with the collection of stored patterns and assigning a weight to each stored pattern; at each pattern recognition weighing unit, matching the unfiltered signal at about the detected third mathematical deviation with the collection of stored patterns and assigning a weight to each stored pattern; inputting each stored pattern and the assigned weights of the stored pattern to an expert system ranking unit; and at the expert system ranking unit, selecting one of the stored patterns by applying a predefined set of generalized rules.
Claim 36
Independent Claim
368 words
A method for analyzing a signal comprising the steps of: receiving an unfiltered signal at a noise filter unit; passing the unfiltered signal through a non-adaptive noise filter to generate a first filtered data signal; passing the unfiltered signal through an adaptive TRAPS filter to generate a second filtered data signal; passing the unfiltered signal, the first filtered data signal and the second filtered data signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a first pattern that is a function of the first filtered data signal; at the adaptive pattern recognition unit, predicting a second pattern that is a function of the second filtered data signal; at the adaptive pattern recognition unit, predicting a third pattern that is a function of the unfiltered signal; detecting a first mathematical deviation in the first filtered data signal by comparing the first filtered data signal with the first predicted pattern; detecting a second mathematical deviation in the second filtered data signal by comparing the second filtered data signal with the second predicted pattern; detecting a third mathematical deviation in the unfiltered signal by comparing the unfiltered signal with the third predicted pattern; flagging the first mathematical deviation, the second mathematical derivation and the third mathematical deviation; passing the first filtered data signal, the second filtered data signal and the unfiltered signal through at least two pattern recognition weighing units; at each pattern recognition weighing unit, matching the first filtered data signal at about the detected first mathematical deviation with a collection of stored patterns and assigning a weight to each stored pattern; at each pattern recognition weighing unit, matching the second filtered data signal at about the detected second mathematical deviation with the collection of stored patterns and assigning a weight to each stored pattern; at each pattern recognition weighing unit, matching the unfiltered signal at about the detected third mathematical deviation with the collection of stored patterns and assigning a weight to each stored pattern; inputting each stored pattern and the assigned weights of the stored pattern to an expert system ranking unit; and at the expert system ranking unit, selecting one of the stored patterns by applying a predefined set of generalized rules.
Claim 37
Dependent Claim
41 words
The method of claim 29 where the at least two pattern recognition weighing units comprise a first pattern recognition weighing unit being an entopic differential weighing unit and a second pattern recognition weighing unit being an adaptive pattern recognition weighing unit.
Claim 38
Dependent Claim
28 words
The method of claim 29 further comprising the step of, at the expert system ranking unit, ordering the stored patterns by applying the predefined set of generalized rules.
Claim 39
Dependent Claim
56 words
The method of claim 29 wherein the step of matching the first filtered data signal at about the detected first mathematical deviation with the collection of stored patterns further comprises the step of matching the first filtered data signal at about the detected first mathematical deviation with the collection of stored patterns using self similarity matching.
Claim 40
Dependent Claim
56 words
The method of claim 29 wherein the step of matching the second filtered data signal at about the detected second mathematical deviation with a collection of stored patterns further comprises the step of matching the second filtered data signal at about the detected second mathematical deviation with the collection of stored patterns using self similarity matching.
Claim 41
Dependent Claim
54 words
The method of claim 29 wherein the step of matching the unfiltered data signal at about the detected third mathematical deviation with a collection of stored patterns further comprises the step of matching the unfiltered data signal at about the detected third mathematical deviation with the collection of stored patterns using self similarity matching.
Claim 42
Independent Claim
146 words
A method for analyzing a signal representing stock prices comprising the steps of: receiving, at a noise filter unit, a signal representative of the price of stocks over time; passing the signal through a noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a pattern that is a function of the filtered signal; detecting a mathematical deviation in the filtered signal by comparing the filtered signal with the predicted pattern; and flagging the detected mathematical deviation; passing the filtered signals through at a pattern recognition weighing unit; at the pattern recognition weighing unit, matching the filtered signal at about the detected mathematical deviation with a collection of stored patterns; and at the pattern recognition unit, determining, for a particular stock, whether the stock is in an uptrend or in a downtrend.
Claim 43
Independent Claim
146 words
A method for analyzing a signal representing stock prices comprising the steps of: receiving, at a noise filter unit, a signal representative of the price of stocks over time; passing the signal through a noise filter to generate a filtered signal; passing the filtered signal through an adaptive pattern recognition unit; at the adaptive pattern recognition unit, predicting a pattern that is a function of the filtered signal; detecting a mathematical deviation in the filtered signal by comparing the filtered signal with the predicted pattern; and flagging the detected mathematical deviation; passing the filtered signals through at a pattern recognition weighing unit; at the pattern recognition weighing unit, matching the filtered signal at about the detected mathematical deviation with a collection of stored patterns; and at the pattern recognition unit, determining, for a particular stock, whether the stock is in an uptrend or in a downtrend.
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