Patent Document ID: 9870768
Application ID: 15262785

Base Claim:
1. A computer automated subject estimation system comprising: a processor; and a memory having a computer program stored thereon, the computer program causing the processor to execute a convolution neural network, wherein the convolution neural network includes: a convolutional layer including one or more topic-dependent convolutional layers and one topic-independent convolutional layer, each of the one or more topic-dependent convolutional layers performing, on an input of a word-string vector sequence corresponding to dialog text transcribed from a dialog, a convolution operation dependent on a topic, and the topic-independent convolutional layer performing, on the input of the word-string vector sequence, a convolution operation not dependent on the topic, a pooling layer performing a pooling process on outputs of the convolutional layer, and a fully connected layer performing a full connection process on outputs of the pooling layer and estimating a subject label of the dialog.

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Claim 3:
3. The computer automated subject estimation system according to claim 1 , wherein in the convolutional neural network, by using, as learning data, learning dialog text that is transcribed from a dialog and in which time-series text of the dialog is pre-divided into segments for respective topics and labels for the corresponding topics are pre-given to the respective divided segments, each of the one or more topic-dependent convolutional layers is made to learn first weights so as to perform a corresponding convolution operation dependent on each topic on which the topic-dependent convolutional layer is dependent, and the topic-independent convolutional layer is made to learn second weights so as to perform a convolution operation not dependent on the topic on which the topic-dependent convolutional layer is dependent.