Source: http://www.google.com/patents/US7797257?dq=oakley+5,387,949
Timestamp: 2015-08-29 06:27:42
Document Index: 461772211

Matched Legal Cases: ['Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60']

Patent US7797257 - System for providing data analysis services using a support vector machine ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA computer system for performing data analysis services using a support vector machine for analyzing data received from a remote source on a distributed network includes a server in communication with the distributed network for receiving a data set and a financial account identifier associated with...http://www.google.com/patents/US7797257?utm_source=gb-gplus-sharePatent US7797257 - System for providing data analysis services using a support vector machine for processing data received from a remote sourceAdvanced Patent SearchPublication numberUS7797257 B2Publication typeGrantApplication numberUS 11/926,129Publication dateSep 14, 2010Filing dateOct 29, 2007Priority dateMay 1, 1998Fee statusLapsedAlso published asUS6714925, US6882990, US7542959, US8275723, US20050165556, US20080033899, US20080059392, US20100256988Publication number11926129, 926129, US 7797257 B2, US 7797257B2, US-B2-7797257, US7797257 B2, US7797257B2InventorsStephen Barnhill, Isabelle Guyon, Jason WestonOriginal AssigneeHealth Discovery CorporationExport CitationBiBTeX, EndNote, RefManPatent Citations (28), Non-Patent Citations (11), Referenced by (5), Classifications (34), Legal Events (6) External Links: USPTO, USPTO Assignment, EspacenetSystem for providing data analysis services using a support vector machine for processing data received from a remote source
US 7797257 B2Abstract
A computer system for performing data analysis services using a support vector machine for analyzing data received from a remote source on a distributed network includes a server in communication with the distributed network for receiving a data set and a financial account identifier associated with the remote source. The server communicates over the distributed network with a financial institution to receive funds from a financial account identified by the financial account identifier. A processor receives one or more data sets from the remote source and pre-processes the data to enhance meaning within the data set. The pre-processed data is used to train and test a support vector machine for recognizing patterns within the data. Live data is processed using the trained and tested support vector machine to generate an output which is transmitted to the remote source after the server confirms that payment for the data processing service has been received.
1. A computer system for providing data analysis services using a support vector machine for analyzing data received from a remote source, the system comprising:
a server in communication with a distributed network for receiving a data set and a financial account identifier from a remote source, the remote source also in communication with the distributed network, wherein the server is further operable for communicating with a financial institution in order to receive funds from a financial account identified by the financial account identifier;
one or more storage devices in communication with the server for storing the data set;
a processor for executing a support vector machine, the processor further operable for:
collecting the data set from the one or more storage devices;
pre-processing the data set to enhance meaning within the data set, wherein meaning is enhanced by one or more of cleaning the data, transforming the data and expanding the data;
before or after pre-processing the data set, dividing the pre-processed data set into a training set and a test set;
processing the training set using the support vector machine so as to train the support vector machine;
in response to training of the support vector machine, processing the test data set using the trained support vector machine in order to test the support vector machine;
in response to receiving a test output from the trained support vector machine indicating that an optimal solution was achieved, collecting a live data set;
pre-processing the live data set to enhance meaning within the live data set;
inputting the live data set into the tested and trained support vector machine to produce a live output comprising a recognized pattern within the live data set;
post-processing the live output to generate an alphanumerical classifier corresponding to the recognized pattern; and
transmitting the alphanumerical classifier to the server;
wherein the server is further operable for transmitting the alphanumerical classifier to the remote source or another remote source after ensuring that funds from the financial account have been secured.
2. The system of claim 1, wherein the financial institution is a bank.
3. The system of claim 1, wherein the financial institution is a credit card company.
4. The system of claim 1, wherein the remote source is a retailer and the data set comprises inventory and audit data.
5. The system of claim 1, wherein the remote source is a medical laboratory and the data set comprises medical data.
6. The system of claim 1, further comprising a firewall disposed between the server and the processor.
7. The system of claim 1, wherein the support vector machine performs recursive feature elimination on the training data and the test data to identify a subset of determinative features within a large number of features that describe the data set, and wherein the processor is further operable for generating a ranked listing of determinative features.
8. A computer system for providing data analysis services over a distributed network, the system comprising:
a remote source in communication with the distributed network;
a server in communication with the distributed network for receiving a data set and a financial account identifier from the remote source, wherein the server is further operable for communicating with a financial institution over the distributed network in order to receive funds from a financial account identified by the financial account identifier;
inputting the live data set into the tested and trained support vector machine to produce a live output comprising a recognized pattern within the live data set; and
transmitting the recognized pattern to the server;
wherein the server is further operable for transmitting the recognized pattern to the remote source or another remote source after ensuring that funds from the financial account have been secured.
9. The system of claim 8, wherein the financial institution is a bank.
10. The system of claim 8, wherein the financial institution is a credit card company.
11. The system of claim 8, wherein the remote source is a retailer and the data set comprises inventory and audit data.
12. The system of claim 8, wherein the remote source is a medical laboratory and the data set comprises medical data.
13. The system of claim 8, further comprising a firewall disposed between the server and the processor.
14. The system of claim 8, wherein the support vector machine performs recursive feature elimination on the training data and the test data to identify a subset of determinative features within a large number of features that describe the data set, and wherein the processor is further operable for generating a ranked listing of determinative features. Description
This application is a continuation of application Ser. No. 11/033,570, filed Jan. 11, 2005, which is a continuation of application Ser. No. 09/633,410, filed Aug. 7, 2000, now issued as U.S. Pat. No. 6,882,990, which is a continuation-in-part of U.S. patent application Ser. No. 09/303,386, now abandoned; Ser. No. 09/303,387, now issued as U.S. Pat. No. 6,128,608; U.S. Ser. No. 09/303,389, now abandoned; U.S. Ser. No. 09/305,345, now issued as U.S. Pat. No. 6,157,921; all filed May 1, 1999 and each of which claim the benefit of U.S. Provisional Application No. 60/083,961, filed May 1, 1998; and is also a continuation-in-part of U.S. patent application Ser. No. 09/568,301, filed May 9, 2000, now issued as U.S. Pat. No. 6,427,141, and of U.S. patent application Ser. No. 09/578,0111, filed May 24, 2000, now issued as U.S. Pat. No. 6,658,395, and also claims the benefit of U.S. Provisional Patent Application No. 60/161,806, filed Oct. 27, 1999; of U.S. Provisional Patent Application No. 60/168,703, filed Dec. 2, 1999; of U.S. Provisional Patent Application No. 60/184,596, filed Feb. 24, 2000; and of U.S. Provisional Patent Application No. 60/191,219, filed Mar. 22, 2000.
The gene chips are capable of containing large arrays of oligonucleotides on very small chips. A variety of methods for measuring hybridization intensity data to determine which probes are hybridizing is known in the art. Methods for detecting hybridization include fluorescent, radioactive, enzymatic, chemoluminescent, bioluminescent and other detection systems.
Older, but still usable, methods such as gel electrophoresis and hybridization to gel blots or dot blots are also useful for determining genetic sequence information. Capture and detection systems for solution hybridization and in situ hybridization methods are also used for determining information about a genome. Additionally, former and currently used methods for defining large parts of genomic sequences, such as chromosome walking and phage library establishment, are used to gain knowledge about genomes.
Within a support vector machine, the dimensionally of the feature space may be huge. For example, a fourth degree polynomial mapping function causes a 200 dimensional input space to be mapped into a 1.6 billionth dimensional feature space. The kernel trick and the Vapnik-Chervonenkis dimension allow the support vector machine to thwart the “curse of dimensionality” limiting other methods and effectively derive generalizable answers from this very high dimensional feature space. Patent applications directed to support vector machines include, U.S. patent application Ser. Nos. 09/303,386; 09/303,387; 09/303,389; 09/305,345; all filed May 1, 1999; and U.S. patent application Ser. No. 09/568,301, filed May 9, 2000; and U.S. patent application Ser. No. 09/578,011, filed May 24, 2000 and also claims the benefit of U.S. Provisional Patent Application No. 60/161,806, filed Oct. 27, 1999; of U.S. Provisional Patent Application No. 60/168,703, filed Dec. 2, 1999; of U.S. Provisional Patent Application No. 60/184,596, filed Feb. 24, 2000; and of U.S. Provisional Patent Application Ser. No. 60/191,219, filed Mar. 22, 2000; all of which are herein incorporated in their entireties.
Furthermore, what are needed are methods and compositions for treating the diseases and other changes in biological systems that are identified by the support vector machine. Once patterns or the relationships between the data are identified by the support vector machines of the present invention and are used to detect or diagnose a particular disease state, what is needed are diagnostic tests, including gene chips and test of bodily fluids or bodily changes, and methods and compositions for treating the condition.
In another exemplary embodiment, systems and methods are provided for optimally categorizing a continuous variable. A data set representing a continuous variable comprises data points that each comprise a sample from the continuous variable and a class identifier. A number of distinct class identifiers within the data set is determined and a number of candidate bins is determined based on the range of the samples and a level of precision of the samples within the data set. Each candidate bin represents a sub-range of the samples. For each candidate bin, the entropy of the data points falling within the candidate bin is calculated. Then, for each sequence of candidate bins that have a minimized collective entropy, a cutoff point in the range of samples is defined to be at the boundary of the last candidate bin in the sequence of candidate bins. As an iterative process the collective entropy for different combinations of sequential candidate bins may be calculated.
In still another exemplary embodiment, a system and method are provided for enhancing knowledge discovery from data using a learning machine in general and a support vector machine in particular in a distributed network environment. A customer may transmit training data, test data and live data to a vendor's server from a remote source, via a distributed network. The customer may also transmit to the server identification information such as a user name, a password and a financial account identifier. The training data, test data and live data may be stored in a storage device. Training data may then be pre-processed in order to add meaning thereto. Pre-processing data may involve transforming the data points and/or expanding the data points. By adding meaning to the data, the learning machine is provided with a greater amount of information for processing. With regard to support vector machines in particular, the greater the amount of information that is processed, the better generalizations about the data that may be derived. The learning machine is therefore trained with the pre-processed training data and is tested with test data that is pre-processed in the same manner. The test output from the learning machine is post-processed in order to determine if the knowledge discovered from the test data is desirable. Post-processing involves interpreting the test output into a format that may be compared with the test data. Live data is pre-processed and input into the trained and tested learning machine. The live output from the learning machine may then be post-processed into a computationally derived alphanumerical classifier for interpretation by a human or computer automated process. Prior to transmitting the alpha numerical classifier to the customer via the distributed network, the server is operable to communicate with a financial institution for the purpose of receiving funds from a financial account of the customer identified by the financial account identifier.
FIGS. 13A, 13B, 13C and 13D graphically illustrate use of a linear discriminant classifier 13A) Separation of the training examples with an SVM. 13B) Separation of the training and test examples with the same SVM. 13C) Separation of the training examples with the baseline method. 13D) Separation of the training and test examples with the baseline method.
FIGS. 16A and 16B show the of gene expression values across genes for all tissue samples.
FIGS. 33A, 33B, 33C and 33D shows the best set of 16 genes for the leukemia data.
The following detailed description utilizes a number of acronyms, which are generally well known in the art. While definitions are typically provided with the first instance of each acronym, for convenience, Table 1A and Table 1B below provide a list of the acronyms and abbreviations used along with their respective definitions. Table 1A lists acronyms and abbreviations generally associated with computer technology and statistics, whereas Table 1B lists acronyms and abbreviations generally associated with biotechnology.