Patent US5267151A successfully refreshed from Google Patents.
US5267151A
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US
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Method and apparatus for detecting and identifying a condition
Assignee: Individual
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Filing: 1990-09-07
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Granted: 1993-11-30
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Expires: 2010-09-07
Abstract
Abstract A method and apparatus for sensing and classifying a condition of interest in a system from background noise in which a parameter representative of the condition of interest is sensed and an electrical signal representative of the sensed parameter is produced. The electrical signal is converted into a digital signal, this digital signal containing a signal of interest representative of the condition of interest and background noise. The digital signal is received by an artificial neural network which filters out the background noise to produce a filtered signal from the digital signal, and classifies the signal of interest from the filtered signal to produce an output representative of the classified signal.
Inventors
- 👤 Frederic M. Ham
- 👤 Glenn M. Cohen
- 👤 Samuel P. Kozaitis
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-25 13:55
CPC Classifications
G; G01; G01N; G01N21/00; G01N21/17; G01N21/25; G01N21/31; G01N21/35; G01N21/3577; G06; G06N; G06N3/00; G06N3/02; G06N3/04; G06N3/045; G06N3/0499; G06N3/08; G06N3/09; G01N2201/00; G01N2201/12; G01N2201/129; G01N2201/1296; G06F; G06F2218/00; G06F2218/02; G06F2218/12; Y; Y10; Y10S; Y10S128/00; Y10S128/92; Y10S128/922
Claims Analysis
Total Claims: 30
Claim 1
Independent Claim
73 words
A method of filtering and classifying a signal of interest from background noise, comprising: receiving in an artificial neural network a digital signal containing a signal of interest and background noise; filtering out the background noise to produce a filtered signal from the digital signal in a feedforward perceptron of the artificial neural network; and classifying the signal of interest from the filtered signal in a counterpropagation network of the artificial neural network.
Claim 2
Independent Claim
73 words
A method of filtering and classifying a signal of interest from background noise, comprising: receiving in an artificial neural network a digital signal containing a signal of interest and background noise; filtering out the background noise to produce a filtered signal from the digital signal in a feedforward perceptron of the artificial neural network; and classifying the signal of interest from the filtered signal in a counterpropagation network of the artificial neural network.
Claim 3
Dependent Claim
16 words
The method of claim 1, wherein the counterpropagation network is performance invariant to threshold decision settings.
Claim 4
Dependent Claim
16 words
The method of claim 1, wherein the signal of interest is representative of a biological condition.
Claim 5
Dependent Claim
14 words
The method of claim 3, wherein the biological condition is glucose concentration in blood.
Claim 6
Dependent Claim
16 words
The method of claim 1, wherein the feedforward perceptron is a feedforward perceptron trained by backpropagation.
Claim 7
Independent Claim
122 words
A method of sensing and classifying a condition of interest in a system from background noise, comprising: sensing a parameter representative of the condition of interest and producing an electrical signal representative of the sensed parameter; converting the electrical signal into a digital signal, said digital signal containing a signal of interest representative of the condition of interest and background noise; receiving in an artificial neural network the digital signal containing the signal of interest and the background noise; filtering out the background noise in the artificial neural network to produce a filtered signal from the digital signal; and classifying the signal of interest from the filtered signal in the artificial neural network to produce an output representative of the classified signal.
Claim 8
Independent Claim
122 words
A method of sensing and classifying a condition of interest in a system from background noise, comprising: sensing a parameter representative of the condition of interest and producing an electrical signal representative of the sensed parameter; converting the electrical signal into a digital signal, said digital signal containing a signal of interest representative of the condition of interest and background noise; receiving in an artificial neural network the digital signal containing the signal of interest and the background noise; filtering out the background noise in the artificial neural network to produce a filtered signal from the digital signal; and classifying the signal of interest from the filtered signal in the artificial neural network to produce an output representative of the classified signal.
Claim 9
Dependent Claim
18 words
The method of claim 6, wherein the step of sensing includes performing infrared absorption and evanescent field spectroscopy.
Claim 10
Dependent Claim
16 words
The method of claim 6, wherein the step of filtering is performed by a feedforward perceptron.
Claim 11
Dependent Claim
16 words
The method of claim 8 wherein the step of classifying is performed by a counterpropagation network.
Claim 12
Independent Claim
61 words
A device for filtering and classifying a signal of interest from background noise, comprising: means for receiving in an artificial neural network a digital signal containing a signal of interest and background noise; means for filtering out the background noise from the digital signal to produce a filtered signal; and means for classifying the signal of interest from the filtered signal.
Claim 13
Independent Claim
61 words
A device for filtering and classifying a signal of interest from background noise, comprising: means for receiving in an artificial neural network a digital signal containing a signal of interest and background noise; means for filtering out the background noise from the digital signal to produce a filtered signal; and means for classifying the signal of interest from the filtered signal.
Claim 14
Dependent Claim
14 words
The device of claim 10, wherein the means for filtering is a feedforward perceptron.
Claim 15
Dependent Claim
14 words
The device of claim 11, wherein the means for classifying is a counterpropagation network.
Claim 16
Dependent Claim
14 words
The device of claim 10, wherein the means for classifying is a counterpropagation network.
Claim 17
Independent Claim
132 words
A device for sensing and classifying a condition of interest in a system from background noise, comprising: means for sensing a parameter representative of the condition of interest and producing an electrical signal representative of the sensed parameter; means for converting the electrical signal into a digital signal, said digital signal containing a signal of interest representative of the condition of interest and background noise; means for receiving in an artificial neural network the digital signal containing the signal of interest and the background noise; means for filtering out the background noise in the artificial neural network to produce a filtered signal from the digital signal; and means for classifying the signal of interest from the filtered signal in the artificial neural network to produce an output representative of the classified signal.
Claim 18
Independent Claim
132 words
A device for sensing and classifying a condition of interest in a system from background noise, comprising: means for sensing a parameter representative of the condition of interest and producing an electrical signal representative of the sensed parameter; means for converting the electrical signal into a digital signal, said digital signal containing a signal of interest representative of the condition of interest and background noise; means for receiving in an artificial neural network the digital signal containing the signal of interest and the background noise; means for filtering out the background noise in the artificial neural network to produce a filtered signal from the digital signal; and means for classifying the signal of interest from the filtered signal in the artificial neural network to produce an output representative of the classified signal.
Claim 19
Dependent Claim
14 words
The device of claim 14, wherein the means for filtering is a feedforward perceptron.
Claim 20
Dependent Claim
14 words
The device of claim 15, wherein the means for classifying is a counterpropagation network.
Claim 21
Dependent Claim
14 words
The device of claim 14, wherein the means for classifying is a counterpropagation network.
Claim 22
Independent Claim
120 words
A device for measuring a condition of a biological system, comprising: a sensor that senses a parameter representative of the condition of interest in the biological system, said sensor producing a signal representative of the sensed parameter and containing background noise; a converter coupled to the sensor to convert the signal produced by the sensor to a digital signal, said digital signal containing a signal of interest and background noise; and an artificial neural network coupled to the converter, said artificial neural network filtering the background noise from the digital signal to produce a filtered signal and classifying the signal of interest from the filtered signal in the artificial neural network to produce an output representative of the classified signal.
Claim 23
Independent Claim
120 words
A device for measuring a condition of a biological system, comprising: a sensor that senses a parameter representative of the condition of interest in the biological system, said sensor producing a signal representative of the sensed parameter and containing background noise; a converter coupled to the sensor to convert the signal produced by the sensor to a digital signal, said digital signal containing a signal of interest and background noise; and an artificial neural network coupled to the converter, said artificial neural network filtering the background noise from the digital signal to produce a filtered signal and classifying the signal of interest from the filtered signal in the artificial neural network to produce an output representative of the classified signal.
Claim 24
Dependent Claim
18 words
The device of claim 18, wherein the sensor includes means for performing infrared absorption and evanescent field spectroscopy.
Claim 25
Dependent Claim
48 words
The device of claim 19, wherein the sensor includes an infrared waveguide, an infrared source, and a detector, said infrared source acting on the biological system and providing an infrared signal via said infrared waveguide to the detector, said detector converting said infrared signal into an electronic signal.
Claim 26
Dependent Claim
47 words
The device of claim 20, wherein the artificial neural network includes a feedforward perceptron coupled to the converter and which performs the filtering by the artificial neural network, and a counterpropagation network coupled to the feedforward perceptron and which performs the classifying by the artificial neural network.
Claim 27
Dependent Claim
25 words
The device of claim 21, wherein the condition of the biological system to be measured is concentration of specific types of molecules in a fluid.
Claim 28
Dependent Claim
21 words
The device of claim 21, wherein the condition of the biological system to be measured is concentration of glucose in blood.
Claim 29
Dependent Claim
12 words
The device of claim 23, wherein the device is implantable in vivo.
Claim 30
Dependent Claim
12 words
The device of claim 18, wherein the device is implantable in vivo.
References Cited (Backward Citations)
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1996-11-29 | 1997-05-31 | 1997-11-30 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2000-11-29 | 2001-05-31 | 2001-11-30 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2004-11-29 | 2005-05-31 | 2005-11-30 | $7700.00 | Window Closed / Lapsed |