Anomaly recognition method for data streams

Data processing: speech signal processing – linguistics – language – Speech signal processing – For storage or transmission

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C704S236000, C704S239000

Reexamination Certificate

active

07546236

ABSTRACT:
This invention identifies anomalies in a data stream, without prior training, by measuring the difficulty in finding similarities between neighborhoods in the ordered sequence of elements. Data elements in an area that is similar to much of the rest of the scene score low mismatches. On the other hand a region that possesses many dissimilarities with other parts of the ordered sequence will attract a high score of mismatches. The invention makes use of a trial and error process to find dissimilarities between parts of the data stream and does not require prior knowledge of the nature of the anomalies that may be present. The method avoids the use of processing dependencies between data elements and is capable of a straightforward parallel implementation for each data element. The invention is of application in searching for anomalous patterns in data streams, which include audio signals, health screening and geographical data. A method of error correction is also described.

REFERENCES:
patent: 4646352 (1987-02-01), Asai et al.
patent: 5113454 (1992-05-01), Marcantonio et al.
patent: 5200820 (1993-04-01), Gharavi
patent: 5303885 (1994-04-01), Wade
patent: 5790413 (1998-08-01), Bartusiak et al.
patent: 5825016 (1998-10-01), Nagahata et al.
patent: 5867813 (1999-02-01), Di Pietro et al.
patent: 5978027 (1999-11-01), Takeda
patent: 6094507 (2000-07-01), Monden
patent: 6111984 (2000-08-01), Fukasawa
patent: 6240208 (2001-05-01), Garakani et al.
patent: 6266676 (2001-07-01), Yoshimura et al.
patent: 6282317 (2001-08-01), Luo et al.
patent: 6304298 (2001-10-01), Steinberg et al.
patent: 6389417 (2002-05-01), Shin et al.
patent: 6499009 (2002-12-01), Lundberg et al.
patent: 6778699 (2004-08-01), Gallagher
patent: 6934415 (2005-08-01), Steintiford
patent: 2001/0013895 (2001-08-01), Aizawa et al.
patent: 2002/0081033 (2002-06-01), Stentiford
patent: 2002/0126891 (2002-09-01), Osberger
patent: 2005/0031178 (2005-02-01), Park
patent: 2005/0074806 (2005-04-01), Skierczynski et al.
patent: 2005/0169535 (2005-08-01), Stentiford
patent: 2006/0050993 (2006-03-01), Stentiford
patent: 0098152 (1984-01-01), None
patent: 1126411 (2001-08-01), None
patent: 1286539 (2003-02-01), None
patent: 1417721 (1975-12-01), None
patent: 3-238533 (1991-10-01), None
patent: 2002-50066 (2002-02-01), None
patent: WO 82/01434 (1982-04-01), None
patent: WO 90/03012 (1990-03-01), None
patent: WO 99/05639 (1999-02-01), None
patent: WO 99/60517 (1999-11-01), None
patent: WO 00/33569 (2000-06-01), None
patent: WO 01/31638 (2001-05-01), None
patent: WO 01/61648 (2001-08-01), None
patent: WO 02/21446 (2002-03-01), None
patent: WO 02/098137 (2002-12-01), None
patent: WO 03/081523 (2003-10-01), None
patent: WO 03/081577 (2003-10-01), None
patent: WO 2004/042645 (2004-05-01), None
patent: WO 2004/057493 (2004-08-01), None
patent: WO 2005/057490 (2005-06-01), None
patent: WO 2006/030173 (2006-03-01), None
International Search Report dated Mar. 18, 2002.
Lutton et al., “Contribution to the Determination of Vanishing Points Using Hough Transform”, 1994 IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, No. 4, Apr. 1994, pp. 430-438.
McLean et al., “Vanishing Point Detection by Line Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, No. 11, Nov. 1995, pp. 1090-1095.
Koizumi et al., “A New Optical Detector for a High-Speed AF Control”, 1996 IEEE, pp. 1055-1061.
Itti et al., “Short Papers: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, No. 11, Nov. 1998, pp. 1254-1259.
Shufelt, “Performance Evaluation and Analysis of Vanishing Point Detection Techniques”, In Analysis and Machine Intelligence, vol. 21, No. 3, Mar. 1999, pp. 282-288.
Wixson, “Detecting Salient Motion by Accumulating Directionally-Consistent Flow”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, No. 8, Aug. 2000, pp. 774-780.
Privitera et al., “Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, No. 9, Sep. 2000, pp. 970-982.
Smeulders et al., “Content-Based Image Retrieval at the End of the Early Years”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, No. 12, Dec. 2000, pp. 1349-1380.
Vailaya et al., “Image Classification for Content-Based Indexing”, IEEE Transactions on Image Processing, vol. 10, No. 1, Jan. 2001, pp. 117-130.
Almansa et al., “Vanishing Point Detection Without Any A Priori Information”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, No. 4, Apr. 2003, pp. 502-507.
Santini et al., “Similarity Matching”, Proc 2ndAsian Conf on Computer Vision, pp. II 544-548, IEEE, 1995.
Rui et al., “A Relevance Feedback Architecture for Content-Based Multimedia Information Retrieval Systems”, 1997 IEEE, pp. 82-89.
Walker et al., “Locating Salient Facial Features Using Image Invariants”, Proc. 3rdIEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 242-247.
Mahlmeister et al., “Sample-guided Progressive Image Coding”, Proc. Fourteenth Int. Conference on Pattern Recognition, Aug. 16-20, 1998, pp. 1257-1259, vol. 2.
Osberger et al., “Automatic Identification of Perceptually Important Regions in an Image”, Proc. Fourteenth Int. Conference on Pattern Recognition, Aug. 16-20, 1998, pp. 701-704, vol. 1.
Buhmann et al., “Dithered Colour Quantisation”, Eurographics 98, Sep. 1998, http://opus.fu-bs.de/opus/volltexte/2004/593/pdf/TR-tubs-cq-1998-01.pdf.
Rui et al., “Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, No. 5, Sep. 1998, pp. 644-655.
Gallet et al., “A Model of the Visual Attention to Speed up Image Analysis”, Proceedings of the 1998 IEEE International Conference on Image Processing (ICIP-98), Chicago, Illinois, Oct. 4-7, 1998, IEEE Computer Society, 1998, ISBAN-08186-8821-1, vol. 1, pp. 246-250.
Curtis et al., “Metadata—The Key to Content Management Services”, 3rdIEEE Metadata Conference, Apr. 6-7, 1999.
Stentiford, “Evolution: The Best Possible Search Algorithm?”, BT Technology Journal, vol. 18, No. 1, Jan. 2000, (Movie Version).
Rother, “A New Approach for Vanishing Point Detection in Architectural Environments”, 11thBritish Machine Vision Conference, Bristol, UK, Sep. 2000, http://www.bmva.ac.uk/bmvc/2000/papers/p39.pdf.
Sebastian et al., “Recognition of Shapes by Editing Shock Graphs”, Proc. ICCV 2001, pp. 755-762.
Stentiford et al., “Automatic Identification of Regions of Interest with Application to the Quantification of DNA Damage in Cells”, Human Vision and Electronic Imaging VII, B.E. Rogowitz, T.N. Pappas, Editors, Proc. SPIE vol. 4662, pp. 244-253, San Jose, Jan. 20-26, 2002.
Xu et al., “Video Summarization and Semantics Editing Tools”, Storage and Retrieval for Media Databases, Proc. SPIE, vol. 4315, San Jose, Jan. 21-26, 2001.
Stentiford, “An Estimator for Visual Attention Through Competitive Novelty with Application to Image Compression”, Picture Coding Symposium 2001, Apr. 25-27, 2001, Seoul, Korea, pp. 101-104, http://www.ee.ucl.ac.uk/-fstentif/PCS2001-pdf.
Cantoni et al., “Vanishing Point Detection: Representation Analysis and New Approaches”, 11thInt. Conf. on Image Analysis and Processing, Palermo, Italy, Sep. 26-28, 2001.
Ouerhani et al., “Adaptive Color Image Compression Based on Visual Attention”, Proc. 11thInt. Conference on Image Analysis and Processing, Sep. 26-28, 2001, pp. 416-421.
Russ et al., “Smart Realisation: Delivering Content Smartly”, J. Inst. BT Engineers, vol. 2, Part 4, pp. 12-17

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Anomaly recognition method for data streams does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Anomaly recognition method for data streams, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Anomaly recognition method for data streams will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-4146530

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.