Verdasys, Inc.

Other

Michael Smolsky

Dwayne A Carson

Donato Buccella

A technique for efficient representation of dependencies between electronically-stored documents, such as in an enterprise data processing system. A document distribution path is developed as a directional graph that is a representation of the historic dependencies between documents, which is constructed in real time as documents are created. The system preferably maintains a lossy hierarchical representation of the documents indexed in such a way that allows for fast queries for similar but not necessarily equivalent documents. A distribution path, coupled with a document similarity service, can be used to provide a number of applications, such as a security solution that is capable of finding and restricting access to documents that contain information that is similar to other existing files that are known to contain sensitive information.

Identifying history of modification within large collections of unstructured data

Systems and methods for monitoring baby's or partially disabled patient's level of thermal comfort during his/her rest in bed are provided. Preferably, the system comprises: a sensor of infrared (IR) radiation coupled to electric circuitry, the circuitry includes a transmitter of radio signal, and, as a separate unit, a receiver of this radio signal. The transmitter includes a circuit containing an alarm with an adjustable threshold that turns the alarm on whenever the heat flux radiated by the body of a patient or a baby rises above user-adjustable upper threshold value, or falls below lower threshold value. The system may be combined with a standard, advanced or video baby monitor.

Device and method for monitoring state of thermal comfort of a baby at sleep or a partially disabled patient

A technique for determining when documents stored in digital format in a data processing system are similar. A method compares a sparse representation of two or more documents by breaking the documents into 'chunks' of data of predefined sizes. Selected subsets of the chunks are determined as being representative of data in the documents and coefficients are developed to represent such chunks. Coefficients are then combined into coefficient clusters containing coefficients that are similar according to a predetermined similarity metric. The degree of similarity between documents is then evaluated by counting clusters into which chunks of similar documents fall.

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Brookline, MA, United States of America

Michael Smolsky