SYSTEM FOR PROVIDING DATA ANALYSIS SERVICES USING A SUPPORT VECTOR MACHINE FOR PROCESSING DATA RECEIVED FROM A REMOTE SOURCE

• US 20100256988A1
• Filed: 06/11/2010
• Published: 10/07/2010
• Est. Priority Date: 05/01/1998
• Status: Active Grant

• Current Assignee: Curtis Anderson, Health Discovery Corporation, James Roberts, Joe Mckenzie, Jules B. Paderewski, Julian N. Stern, Memorial Health Systems Incorporated, Timothy P. O'Hayer
• Original Assignee: Health Discovery Corporation
• Inventors: Guyon, Isabelle, Weston, Jason, Barnhill, Stephen
• Granted Patent: US 8,275,723 B2
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• CPC Class Codes: G06F 18/2115 by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/771 Feature selection, e.g. selecting representative features from a multi-dimensional feature space
• CPC Class Codes: G06V 10/774 Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A network-based system is provided for performing data analysis services using a support vector machine for analyzing data received from a remote user connected to the network. The user transmits a data set to be analyzed and along with an account identifier that allows the analysis service provider to collect payment for the processing services. Once payment has been confirmed, the service provider'"'"'s server transmits the analysis results to the remote user.

Claims

1-32. -32. (canceled)

33. A computer system for providing data analysis services accessible via 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 having unknown patterns and an account identifier from the remote source, wherein the server is further operable for communicating over the distributed network with an institution associated with the account identifier in order to provide for payment for the data analysis services from an account identified by the account identifier;

one or more storage devices in communication with the server for storing a training and testing dataset having known patterns and the data set from the remote source;

a processor in communication with the one or more storage devices for executing a support vector machine, the processor further operable for;

pre-processing the training and testing data to clean, transform or expand the data;

training and testing the support vector machine until an optimal solution is achieved;

collecting the data set from the remote source;

pre-processing the data set from the remote source;

inputting the pre-processed data set from the remote source into the tested and trained support vector machine to produce an output comprising a recognized pattern within the data set from the remote source; 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 payment from the account has been secured.

34. The system of claim 33, wherein the financial institution is a bank.

35. The system of claim 33, wherein the financial institution is a credit or debit card company.

36. The system of claim 33, wherein the data set from the remote source comprises inventory and audit data.

37. The system of claim 33, wherein the data set from the remote source comprises insurance data.

38. The system of claim 33, wherein the data set from the remote source comprises stock market or commodity market data.

39. The system of claim 33, wherein the data set from the remote source comprises medical data.

40. The system of claim 39, wherein the remote source comprises a medical laboratory.

41. The system of claim 39, wherein the medical data comprises a mammogram.

42. The system of claim 39, wherein the medical data comprises genomic or proteomic data.

43. The system of claim 39, wherein the medical data comprises clinical case information.

44. The system of claim 39, wherein the medical data comprises data from clinical samples.

45. The system of claim 33, wherein the processor is further operable for executing a feature selection algorithm on the training and testing dataset to identify a subset of determinative features within a large number of features that describe the training and testing dataset and generating a ranked listing of determinative features.

46. The system of claim 45, wherein the feature selection algorithm is recursive feature elimination.

47. The system of claim 33, wherein the distributed network is the Internet.

48. The system of claim 33, wherein the distributed network is an on-demand communications link.

49. A 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 an 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 an institution for conducting a financial transaction in order to receive funds from an account identified by the account identifier;

one or more storage devices in communication with the server for storing the dataset;

a processor for executing a support vector machine, the processor further operable for;

training and testing the support vector machine using a training dataset to provide

a trained support vector machine;

collecting the dataset received from the remote source;

pre-processing the dataset from the remote source;

inputting the dataset from the remote source into the tested and trained support vector machine to produce an output comprising a recognized pattern within the dataset from the remote source; and

transmitting the output to the remote source or another remote source after ensuring that funds from the account have been secured.

50. The system of claim 49, wherein the processor is further operable for post-processing the output to generate an alphanumerical classifier corresponding to the recognized pattern, wherein the alphanumerical classifier is transmitted to the remote source after ensuring that funds from the account have been secured.

51. The system of claim 49, wherein the institution for conducting a financial transaction is a bank.

52. The system of claim 49, wherein the financial institution is a credit or debit card company.

53. The system of claim 49, wherein the data set from the remote source comprises inventory and audit data.

54. The system of claim 49, wherein the data set from the remote source comprises insurance data.

55. The system of claim 49, wherein the data set from the remote source comprises stock market or commodity market data.

56. The system of claim 49, wherein the data set from the remote source comprises medical data.

57. The system of claim 56, wherein the remote source comprises a medical laboratory.

58. The system of claim 56, wherein the medical data comprises a mammogram.

59. The system of claim 56, wherein the medical data comprises genomic or proteomic data.

60. The system of claim 56, wherein the medical data comprises clinical case information.

61. The system of claim 56, wherein the medical data comprises data from clinical samples.

62. The system of claim 49, wherein the processor is further operable for executing a feature selection algorithm on the training dataset to identify a subset of determinative features within a large number of features that describe the training and testing dataset and generating a ranked listing of determinative features.

63. The system of claim 62, wherein the feature selection algorithm is recursive feature elimination.

64. The system of claim 49, wherein the distributed network is the Internet.

65. The system of claim 49, wherein the distributed network is an on-demand communications link.