Neuristics, Inc.

Netapp, Inc.

Datron/transoc, Inc.

Subramaniam Mohan

Venkatesh Narayanan

Timothy L Hutcheson

Bobby R Hunt

Ramanujam Srinivasan

Peter G Wohlmut

Wilson Or

Thomas W Ryan

Kaladhar Voruganti

Steven Robert Kleiman

Gokul Soundararajan

Shailaja Kamila

James Hartwell Holl, Ii

Technology is disclosed for managing network storage services by service level objectives (SLOs). The method receives multiple service level capability (SLC) templates; creates at least one storage service level (SSL) instance using at least one of the SLC templates; provisions a storage object located in a network storage infrastructure based on the SSL instance; and services storage requests using the storage object.

Network storage management at scale using service level objectives

A method and apparatus under software control for pattern recognition utilizes a neural network implementation to recognize two dimensional input images which are sufficiently similar to a database of previously stored two dimensional images. Images are first image processed and subjected to a Fourier transform which yields a power spectrum. An in-class to out-of-class study is performed on a typical collection of images in order to determine the most discriminatory regions of the Fourier transform. A feature vector consisting of the highest order (most discriminatory) magnitude information from the power spectrum of the Fourier transform of the image is formed. Feature vectors are input to a neural network having preferably two hidden layers, input dimensionality of the number of elements in the feature vector and output dimensionality of the number of data elements stored in the database. Unique identifier numbers are preferably stored along with the feature vector. Application of a query feature vector to the neural network will result in an output vector. The output vector is subjected to statistical analysis to determine if a sufficiently high confidence level exists to indicate that a successful identification has been made. Where a successful identification has occurred, the unique identifier number may be displayed.

Pattern recognition system

A pattern recognition method and apparatus utilizes a neural network to recognize input images which are sufficiently similar to a database of previously stored images. Images are first processed and subjected to a Fourier transform which yields a power spectrum. An in-class to out-of-class study is performed on a typical collection of images in order to determine the most discriminatory regions of the Fourier transform. A feature vector consisting of the (most discriminatory) information from the power spectrum of the Fourier transform of the image is formed. Feature vectors are input to a neural network having preferably two hidden layers, input dimensionality of the number of elements in the feature vector and output dimensionality of the number of data elements stored in the database. Unique identifier numbers are preferably stored along with the feature vector. Application of a query feature vector to the neural network results in an output vector. The output vector is subjected to statistical analysis to determine if a sufficiently high confidence level exists to indicate a successful identification whereupon a unique identifier number may be displayed.

Method and apparatus for determining and organizing feature vectors for

Apparatus for generating a feature matrix based on normalized out-class

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Sunnyvale, CA, United States of America

Subramaniam Mohan