Patent Document ID: 20060069955
Application ID: 11179838
Patent Status: 0

Claim One:
1. A sequential data examination method of determining whether or not sequential data including a plurality of types of events belong to one or more specified categories, comprising the steps of: an Eigen co-occurrence matrix set determination step of determining one or more Eigen co-occurrence matrix sets, which are a basis for obtaining feature vectors based on a plurality of learning sequential data to be learned, a profiling co-occurrence matrix conversion step of converting one or more profile-learning sequential data belonging to the one or more categories into one or more profiling co-occurrence matrices, a reference feature vector extraction step of extracting one or more reference feature vectors in respect of the one or more profile-learning sequential data, based on the one or more profiling co-occurrence matrices and the one or more Eigen co-occurrence matrix sets, a testing co-occurrence matrix conversion step of converting testing sequential data to be tested into a testing co-occurrence matrix, a testing feature vector extraction step of extracting a testing feature vector in respect of the testing sequential data to be tested, based on the testing co-occurrence matrix and the one or more Eigen co-occurrence matrix sets, a reference approximate co-occurrence matrix acquisition step of acquiring a plurality of reference approximate co-occurrence matrices having a dimensionality reduced from that of the plurality of Eigen co-occurrence matrices, based on the one or more reference feature vectors and the plurality of Eigen co-occurrence matrices forming the one or more Eigen co-occurrence matrix sets, a reference layered network model construction step of constructing a reference layered network model by extracting one or more events from the plurality of reference approximate co-occurrence matrices, a testing approximate co-occurrence matrix acquisition step of acquiring a plurality of testing approximate co-occurrence matrices having a dimensionality reduced from that of the plurality of Eigen co-occurrence matrices, based on the testing feature vector and the plurality of Eigen co-occurrence matrices forming the one or more Eigen co-occurrence matrix sets, a testing layered network model construction step of constructing a testing layered network model by extracting one or more events from the plurality of testing approximate co-occurrence matrices, and a determination step of determining whether or not the testing sequential data to be tested belong to the one or more categories, based on the reference layered network model and the testing layered network model.