US6882990B1
Expired / Lapsed
US
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Methods of identifying biological patterns using multiple data sets
Assignee: Health Discovery Corp
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Filing: 2000-08-07
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Granted: 2005-04-19
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Expires: 2020-08-07
Abstract
Abstract Systems and methods for enhancing knowledge discovery from data using multiple learning machines in general and multiple support vector machines in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Multiple support vector machines, each comprising distinct kernels, are trained with the pre-processed training data and are tested with test data that is pre-processed in the same manner. The test outputs from multiple support vector machines are compared in order to determine which of the test outputs if any represents a optimal solution. Selection of one or more kernels may be adjusted and one or more support vector machines may be retrained and retested. Optimal solutions based on distinct input data sets may be combined to form a new input data set to be input into one or more additional support vector machine. The methods, systems and devices of the present invention comprise use of Support Vector Machines for the identification of patterns that are important for medical diagnosis, prognosis and treatment. Such patterns may be found in many different datasets. The present invention also comprises methods and compositions for the treatment and diagnosis of medical conditions.
Inventors
- 👤 Stephen Barnhill
- 👤 Isabelle Guyon
- 👤 Jason Weston
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-26 16:13
CPC Classifications
G; G06; G06Q; G06Q10/00; G06Q10/06; G06Q10/063; G06Q10/0637; G06F; G06F18/00; G06F18/20; G06F18/21; G06F18/211; G06F18/2113; G06F18/2115; G06F18/214; G06F18/24; G06F18/241; G06F18/2411; G06N; G06N20/00; G06N20/10; G06Q10/10; G06Q20/00; G06Q20/08; G06Q20/10; G06Q40/00; G06Q40/06; G06T; G06T7/00; G06T7/0002; G06T7/0012; G06V; G06V10/00; G06V10/70; G06V10/764; G06V10/77; G06V10/771; G06V10/774; G16; G16B; G16B40/00; G16B40/20; G16B40/30; G16H; G16H10/00; G16H10/40; G16B25/00; Y; Y02; Y02A; Y02A90/00; Y02A90/10
Claims Analysis
Total Claims: 19
Claim 1
Independent Claim
278 words
A method for enhancing knowledge discovery using multiple support vector machines comprising: pre-processing a first training biological data set and a second training biological data set in order to add dimensionality to each of a plurality of training biological data points; training one or more first support vector machines using the first pre-processed training biological data set, each of the first support vector machines comprising different kernels; training one or more second support vector machines using the second pre-processed training data set, each of the second support vector machines comprising different kernels; pre-processing a first test biological data set in the same manner as was the first training biological data sets and pre-processing a second test biological data set in the same manner as was the second training biological data set; testing each of the first trained support vector machines using the first pre-processed test biological data set and testing each of the second trained support vector machines using the second pre-processed test biological data set; in response to receiving a first test output from each of the first trained support vector machines, comparing each of the first test outputs with each other to determine which if any of the first test outputs is a first optimal solution; in response to receiving a second test output from each of the second trained support vector machines, comparing each of the second test outputs with each other to determine which if any of the second test outputs is a second optimal solution; combining the first optimal solution with the second optimal solution to create a new input data set to be input into one or more additional support vector machines.
Claim 2
Independent Claim
278 words
A method for enhancing knowledge discovery using multiple support vector machines comprising: pre-processing a first training biological data set and a second training biological data set in order to add dimensionality to each of a plurality of training biological data points; training one or more first support vector machines using the first pre-processed training biological data set, each of the first support vector machines comprising different kernels; training one or more second support vector machines using the second pre-processed training data set, each of the second support vector machines comprising different kernels; pre-processing a first test biological data set in the same manner as was the first training biological data sets and pre-processing a second test biological data set in the same manner as was the second training biological data set; testing each of the first trained support vector machines using the first pre-processed test biological data set and testing each of the second trained support vector machines using the second pre-processed test biological data set; in response to receiving a first test output from each of the first trained support vector machines, comparing each of the first test outputs with each other to determine which if any of the first test outputs is a first optimal solution; in response to receiving a second test output from each of the second trained support vector machines, comparing each of the second test outputs with each other to determine which if any of the second test outputs is a second optimal solution; combining the first optimal solution with the second optimal solution to create a new input data set to be input into one or more additional support vector machines.
Claim 3
Dependent Claim
56 words
The method of claim 1 , wherein pre-processing the first training biological data set and the second training biological data set further comprises: determining that at least one of the training biological data points is dirty; and in response to determining that the training biological data point is dirty, cleaning the dirty training biological data point.
Claim 4
Dependent Claim
22 words
The method of claim 2 , wherein cleaning the dirty training biological data point comprises deleting, repairing or replacing the data point.
Claim 5
Dependent Claim
39 words
The method of claim 1 , wherein each training biological data point comprises a vector having one or more original coordinates; and wherein pre-processing the training biological data set comprises adding one or more new coordinates to the vector.
Claim 6
Dependent Claim
31 words
The method of claim 4 , wherein the one or more new coordinates added to the vector are derived by applying a transformation to one or more of the original coordinates.
Claim 7
Dependent Claim
14 words
The method of claim 5 , wherein the transformation is based on expert knowledge.
Claim 8
Dependent Claim
12 words
The method of claim 5 , wherein the transformation is computationally derived.
Claim 9
Dependent Claim
30 words
The method of claim 1 , wherein the training data set comprises a continuous variable; and wherein the transformation comprises optimally categorizing the continuous variable of the training data set.
Claim 10
Dependent Claim
99 words
The method of claim 1 , wherein comparing each of the first test outputs with each other and comparing each of the second test outputs with each other comprises: post-processing each of the test outputs by interpreting each of the test outputs into a common format; comparing each of the first post-processed test outputs with each other to determine which of the first test outputs represents a first lowest global minimum error; and comparing each of the second post-processed lest outputs with each other to determine which of the second test outputs represents a second lowest global minimum error.
Claim 11
Dependent Claim
64 words
The method of claim 1 , wherein the knowledge to be discovered from the data relates to a regression or density estimation; wherein each support vector machine produces a training output comprising a continuous variable; and wherein the method further comprises the step of post-processing each of the training outputs by optimally categorizing the training output to derive cutoff points in the continuous variable.
Claim 12
Dependent Claim
71 words
The method of claim 1 , further comprising the steps of: in response to comparing each of the test outputs with each other, determining that none of the test outputs is the optimal solution; adjusting the different kernels of one or more of the plurality of support vector machines; and in response to adjusting the selection of the different kernels, retraining and retesting each of the plurality of support vector machines.
Claim 13
Dependent Claim
41 words
The method of claim 11 , wherein adjusting the different kernels is performed based on prior performance or historical data and is dependant on the nature of the knowledge to be discovered from the data or the nature of the data.
Claim 14
Dependent Claim
277 words
A computer-readable medium with computer-executable instructions for performing a method for: pre-processing a first training biological data set and a second training biological data set in order to add dimensionality to each of a plurality of training biological data points; training one or more first support vector machines using the first pre-processed training biological data set, each of the first support vector machines comprising different kernels; training one or more second support vector machines using the second pre-processed training data set, each of the second support vector machines comprising different kernels; pre-processing a first test biological data set in the same manner as was the first training biological data sets and pre-processing a second test biological data set in the same manner as was the second training biological data set; testing each of the first trained support vector machines using the first pre-processed test biological data set and testing each of the second trained support vector machines using the second pre-processed test biological data set; in response to receiving a first test output from each of the first trained support vector machines, comparing each of the first test outputs with each other to determine which if any of the first test outputs is a first optimal solution; in response to receiving a second test output from each of the second trained support vector machines, comparing each of the second test outputs with each other to determine which if any of the second test outputs is a second optimal solution; combining the first optimal solution with the second optimal solution to create a new input data set to be input into one or more additional support vector machines.
Claim 15
Dependent Claim
277 words
A computer-readable medium with computer-executable instructions for performing a method for: pre-processing a first training biological data set and a second training biological data set in order to add dimensionality to each of a plurality of training biological data points; training one or more first support vector machines using the first pre-processed training biological data set, each of the first support vector machines comprising different kernels; training one or more second support vector machines using the second pre-processed training data set, each of the second support vector machines comprising different kernels; pre-processing a first test biological data set in the same manner as was the first training biological data sets and pre-processing a second test biological data set in the same manner as was the second training biological data set; testing each of the first trained support vector machines using the first pre-processed test biological data set and testing each of the second trained support vector machines using the second pre-processed test biological data set; in response to receiving a first test output from each of the first trained support vector machines, comparing each of the first test outputs with each other to determine which if any of the first test outputs is a first optimal solution; in response to receiving a second test output from each of the second trained support vector machines, comparing each of the second test outputs with each other to determine which if any of the second test outputs is a second optimal solution; combining the first optimal solution with the second optimal solution to create a new input data set to be input into one or more additional support vector machines.
Claim 16
Dependent Claim
43 words
The computer-readable medium with computer-executable instructions of claim 13 , wherein each training biological data point comprises a vector having one or more original coordinates; and wherein pre-processing the training biological data set comprises adding one or more new coordinates to the vector.
Claim 17
Dependent Claim
34 words
The computer-readable medium with computer-executable instructions of claim 13 , wherein the training data set comprises a continuous variable; and wherein the transformation comprises optimally categorizing the continuous variable of the training data set.
Claim 18
Dependent Claim
103 words
The computer-readable medium with computer-executable instructions of claim 13 , wherein comparing each of the first test outputs with each other and comparing each of the second test outputs with each other comprises: post-processing each of the test outputs by interpreting each of the test outputs into a common format; comparing each of the first post-processed test outputs with each other to determine which of the first test outputs represents a first lowest global minimum error; and comparing each of the second post-processed test outputs with each other to determine which of the second test outputs represents a second lowest global minimum error.
Claim 19
Dependent Claim
75 words
The computer-readable medium with computer-executable instructions of claim 13 , further comprising the steps of: in response to comparing each of the test outputs with each other, determining that none of the test outputs is the optimal solution; adjusting the different kernels of one or more of the plurality of support vector machines; and in response to adjusting the selection of the different kernels, retraining and retesting each of the plurality of support vector machines.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2008-04-18 | 2008-10-18 | 2009-04-19 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2012-04-18 | 2012-10-18 | 2013-04-19 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2016-04-18 | 2016-10-18 | 2017-04-19 | $7700.00 | Window Closed / Lapsed |