Bernardo I Rechea

7937263, May 3, 2011

Dec 1, 2004

11/001654

Jill Carrier - Dorchester MA, US
Alwin B. Carus - Waban MA, US
William F. Cote - Carlisle MA, US
John Dowd - Sudbury MA, US
Kathryn Del La Femina - Ashland MA, US
Alan Frankel - Framingham MA, US
Larissa Lapshina - Shirley MA, US
Bernardo Rechea - Belmont MA, US
Ana Santisteban - Somerville MA, US
Amy J. Uhrbach - Needham MA, US

Dictaphone Corporation - Stratford CT

G06F 17/27
G06F 17/20

704 9, 704 1, 704 10

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.

8036889, Oct 11, 2011

Feb 27, 2006

11/362646

Alwin B. Carus - Waban MA, US
Larissa Lapshina - Shirley MA, US
Bernardo Rechea - Belmont MA, US

Nuance Communications, Inc. - Burlington MA

G10L 15/26
G06F 17/21

704235, 704234, 704244, 715230

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.

20060235687, Oct 19, 2006

Apr 14, 2005

11/105905

Alwin Carus - Waban MA, US
Larissa Lapshina - Shirley MA, US
Bernardo Rechea - Belmont MA, US
Amy Uhrbach - Needham MA, US

Dictaphone Corporation - Stratford CT

G10L 15/00

704252000

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.

20110320189, Dec 29, 2011

Sep 9, 2011

13/228617

Alwin B. Carus - Waban MA, US
Larissa Lapshina - Shirley MA, US
Bernardo Rechea - Belmont MA, US

Dictaphone Corporation - Stratford CT

G06F 17/27

704 9

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.