Philip P Chan, 71Walnut Creek, CA

7769759, Aug 3, 2010

Aug 30, 2004

10/931291

Daniel Gartung - Hillsborough CA, US
Philip Chan - South San Francisco CA, US
John Rotherham - Sunnyvale CA, US

Biz360, Inc. - San Mateo CA

G06F 17/30

707737

Data classification is used to classified input items by associating the input items with one or more classes from a set of one or more classes in a data classification system, including identifying relevant features in an input item to form a feature vector for the input item, receiving at the data classification system an indication of a point-of-view, adjusting the feature vector according to the point-of-view indication or modifying a pattern discriminator (e. g. , trainer and classifier) to inline-process feature vectors depending on the provided point-of-view (e. g. , SVM custom kernels), and classifying the input item into the set of classes according to the point-of-view. The point-of-view data can be introduced either as a pre-process step prior to passing it off to the pattern discrimination algorithm, or can be incorporated directly into the pattern discrimination algorithm if applicable. The pattern discrimination algorithms can detect arbitrary patterns given a similarly prepared dataset during both training and subsequent classification of unclassified documents.

2011012, May 26, 2011

Jun 24, 2010

12/822991

Daniel Gartung - Hillsborough CA, US
Philip Chan - South San Francisco CA, US
John Rotherham - Sunnyvale CA, US

BIZ360, Inc. - San Mateo CA

G06F 17/30

707737, 707E17046

Data classification is used to classified input items by associating the input items with one or more classes from a set of one or more classes in a data classification system, including identifying relevant features in an input item to form a feature vector for the input item, receiving at the data classification system an indication of a point-of-view, adjusting the feature vector according to the point-of-view indication or modifying a pattern discriminator (e.g., trainer and classifier) to inline-process feature vectors depending on the provided point-of-view (e.g., SVM custom kernels), and classifying the input item into the set of classes according to the point-of-view. The point-of-view data can be introduced either as a pre-process step prior to passing it off to the pattern discrimination algorithm, or can be incorporated directly into the pattern discrimination algorithm if applicable. The pattern discrimination algorithms can detect arbitrary patterns given a similarly prepared dataset during both training and subsequent classification of unclassified documents.