Dictaphone Corporation.

Nuance Communications, Inc.

Bernardo Rechea

Alwin B Carus

Larissa Lapshina

Amy J Uhrbach

William F Cote

Jill Carrier

Ana Santisteban

Alan Frankel

Kathryn Del La Femina

Wensheng(Vincent) Han

John Dowd

A system and method for filtering documents to determine section boundaries between dictated and non-dictated text. The system and method identifies portions of a text report that correspond to an original dictation and, correspondingly, those portions that are not part of the original dictation. The system and method include comparing tokenized and normalized forms of the original dictation and the final report, determining mismatches between the two forms, and applying machine-learning techniques to identify document headers, footers, page turns, macros, and lists automatically and accurately.

Systems and methods for filtering dictated and non-dictated sections of documents

The present invention pertains to a system and method for the tokenization of text. The featurizer may be configured to receive input text and convert the input text into tokens. According to one aspect of the invention, the tokens may include only one type of character, the characters selected from the group consisting of letters, numbers, and punctuation. The tokenizer may also include a classifier. The classifier may be configured to receive the tokens from the featurizer. Furthermore, the classifier may be configured to analyze the tokens received from the featurizer to determine if the tokens may be input into a predetermined classification model using a preclassifier. If one of the tokens passes the preclassifier, then the token is classified using the predetermined classification model. Additionally, according to a first aspect of the invention, the tokenizer may also include a finalizer. The finalizer may be configured to receive the tokens and may be configured to produce a final output.

System and method for tokenization of text using classifier models

A method for adaptive automatic error and mismatch correction is disclosed for use with a system having an automatic error and mismatch correction learning module, an automatic error and mismatch correction model, and a classifier module. The learning module operates by receiving pairs of documents, identifying and selecting effective candidate errors and mismatches, and generating classifiers corresponding to these selected errors and mismatches. The correction model operates by receiving a string of interpreted speech into the automatic error and mismatch correction module, identifying target tokens in the string of interpreted speech, creating a set of classifier features according to requirements of the automatic error and mismatch correction model, comparing the target tokens against the classifier features to detect errors and mismatches in the string of interpreted speech, and modifying the string of interpreted speech based upon the classifier features.

System and method for adaptive automatic error correction

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Belmont, MA, United States of America

Bernardo Rechea