Tvworks, LLC

Synaptics Corporation

Comcast Cable Communications Management, LLC

Joseph Decker

Mark A Vickers

Carolyn Wales

James Helman

Brian Bulkowski

Sam Hsu

Brock Wagenaar

John C Platt

Steven J Nowlan

Nada P Matic

The invention provides a method and apparatus that addresses and resolves the issues currently affecting the ability to offer Enhanced TV, in particular, those issues concerning timing and synchronization, interaction with other modules in the STB, and distribution.

Method and apparatus for accessing higher privileged functions from lower privileged functions

Method and apparatus for determining data is available using a virtual time base

Method and apparatus for transmitting enhancement data in data streams

Accessing a higher privileged application function from a lower privileged application

Method and apparatus for transmitting enhancement data in a data stream

Advancing virtual time bases for content

Transmitting enhancement data for video

Processing applications with multiple privilege levels

Transmitting timing information for content in a data stream

Method and apparatus for transmitting data in a data stream

Apparatus for fraud detection for an account includes a plurality of N statistical estimators which estimate the likelihood of a transaction being fraudulent from data about a transaction on the account and transaction history of the account. A statistical estimator is provided which computes an estimate of the likelihood of a particular signature being that of a person authorized to sign on the account from an exemplar of the signature and a history of previous signatures. A combiner produces a combined probability estimate from the plurality of N statistical estimators and the statistical estimator.

Identity verification methods

A system for recognizing handwritten characters, including pre-processing apparatus for generating a set of features for each handwritten character, a neural network disposed for operating on sparse data structures of those features and generating a set of confidence values for each possible character symbol which might correspond to the handwritten character, and post-processing apparatus for adjusting those confidence values and for selecting a character symbol consistent with external knowledge about handwritten characters and the language they are written in. The pre-processing apparatus scales and re-parameterizes the handwritten strokes, encodes the scaled and re-parameterizd strokes into fuzzy membership vectors and binary pointwise data, and combines the vectors and data into a sparse data structure of features. The (nonconvolutional) neural network performs a matrix-vector multiply on the sparse data structure, using only the data for nonzero features collected in that structure, and, for a first layer of that neural network, using only successive chunks of the neural weights. The post-processing apparatus adjusts the confidence values for character symbols using a set of expert rules embodying common-sense knowledge, from which it generates a set of character probabilities for each character position; these character probabilities are combined with a Markov model of character sequence transitions and a dictionary of known words, to produce a final work output for a sequence of handwritten characters.

Handwriting recognition system and method

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San Jose, CA, United States of America

Joseph Decker