Lucent Technologies Inc.

Alcatel Lucent

Craig R Nohl

Christopher J Burges

Jan I Ben

Madjid Sam Mousavi

Theodore Sizer

Katherine Grace August

Ping-Wen Ong

Michelle McNerney

Elizabeth A Shriver

Mark Henry Hansen

Daniel D Lee

Chin-Sheng Chuang

B Ivan Strom

The content of a media program is recognized by analyzing its audio content to extract therefrom prescribed features, which are compared to a database of features associated with identified content. The identity of the content within the database that has features that most closely match the features of the media program being played is supplied as the identity of the program being played. The features are extracted from a frequency domain version of the media program by a) filtering the coefficients to reduce their number, e.g., using triangular filters; b) grouping a number of consecutive outputs of triangular filters into segments; and c) selecting those segments that meet prescribed criteria, such as those segments that have the largest minimum segment energy with prescribed constraints that prevent the segments from being too close to each other. The triangular filters may be log-spaced and their output may be normalized.

Content identification system

A system and method for information searching comprising determination of, in fine granularity, a Community of Interest (COI), further data mining in search results, using at least one of COI and expert preferences to identify important knowledge, formulation and manipulation of results, and summarization of search results into a document like entity with dynamic attributes described. More particularly, the invention relates to a system and method for providing interactive dialogue and iterative search functions to find information on a large network of servers such as the world wide web.

System and method for providing interactive dialogue and iterative search functions to find information

A technique is described for producing field recognition scores that vary according to the position of the errors. In particular, a value-based score for a recognition result is determined from both the individual character recognition scores and an &ldquo;error value&rdquo; that is associated with each character position of the recognition result. In general, this value-based score is computed by adding the error value for each character position of the recognition result. As illustration, an OCR system for scanning checks is adapted to produce field recognition scores that reflect economic value. In this approach, an error in the leftmost digit is &ldquo;worth&rdquo; much more than an error in the rightmost digit.

Value based scoring for optical character recognition

Growing community of inventors

Middletown, NJ, United States of America

Craig R Nohl