International Business Machines Corporation

Tanveer Fathima Mahmood

Fei Wang

David James Beymer

Guy Maring Lohman

Mark Francis Wilding

Rajeev Gupta

Natwar Modani

Laurent Sebastien Mignet

A method for recognizing heart diseases in a cardiac echo video of a heart with an unknown disease using a spatio-temporal disease model derived from a training echo video, comprising the steps of: generating a plurality of training models for heart diseases, wherein the cardiac echo videos are each derived from a known viewpoint and the disease of the heart is known; analyzing the video of the heart with the unknown disease by fitting a model of shape and motion for each frame and combining the results across the frames; and, reporting the disease using a classification method for choosing among the diseases of interest.

Cardiac view recognition and disease recognition

Systems and methods for cardiac view recognition and disease recognition

The invention finds matches in ordered sequences, e.g. program function call stacks from anomalous software program executions for discovering related or identical software flaws and possible known solutions. Call stacks are matched after removing recursive and uninformative subpatterns, e.g. names of common error handling routines, to see if they were generated due to the same possibly known problem. Sequences statistically unlikely to be matches are pruned from the search. Matches found earlier in the sequences may be weighted differently, e.g. to be more important in call stacks, than other matches. Sequences are indexed so that those relevant to a query are identified automatically without detailed exhaustive search. Maximum matches are found by optimizing a cost function based on weighted sequence similarity.

System and method for matching a plurality of ordered sequences with applications to call stack analysis to identify known software problems

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Cupertino, CA, United States of America

Tanveer Fathima Mahmood