The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jun. 02, 2009
Filed:
Feb. 02, 2004
Giovanni Lorenzo Thione, San Francisco, CA (US);
Laurent Denoue, Palo Alto, CA (US);
Martin Henk Van Den Berg, Palo Alto, CA (US);
Giovanni Lorenzo Thione, San Francisco, CA (US);
Laurent Denoue, Palo Alto, CA (US);
Martin Henk Van Den Berg, Palo Alto, CA (US);
Fuji Xerox Co., Ltd., Tokyo, JP;
Abstract
Techniques are provided for determining collaborative notes and automatically recognizing speech, handwriting and other type of information. Domain and optional actor/speaker information associated with the support information is determined. An initial automatic speech recognition model is determined based on the domain and/or actor information. The domain and/or actor/speaker language model is used to recognize text in the speech information associated with the support information. Presentation support information such as slides, speaker notes and the like are determined. The semantic overlap between the support information and the salient non-function words in the recognized text and collaborative user feedback information are used to determine relevancy scores for the recognized text. Grammaticality, well formedness, self referential integrity and other features are used to determine correctness scores. Suggested collaborative notes are displayed in the user interface based on the salient non-function words. User actions in the user interface determine feedback signals. Recognition models such as automatic speech recognition, handwriting recognition are determined based on the feedback signals and the correctness and relevance scores.