Patent Document ID: 9785891
Application ID: 14564138

Base Claim:
1. A computer-implemented method for automatically analyzing a conversational sequence between a plurality of users, the method comprising: receiving, using a data collection module on a computer comprising a processor and memory, signals corresponding to a training dataset including a plurality of data sequences related to the conversational sequence; extracting, using a feature extraction module on the computer, at least one feature from the received training dataset based on predefined feature categories; formulating, using a learning module on the computer, a plurality of tasks for being learned from the training dataset based on the extracted at least one feature, wherein each of the plurality of tasks is related to at least one predefined label; providing, using the learning module on the computer, a model for each of the plurality of formulated tasks, wherein the model includes one or more parameters having a set of parameters common to the plurality of formulated tasks, wherein the set of parameters includes at least one label dependency factor that is an explicit parameter being explicitly shared with each of the plurality of formulated tasks; optimizing, using the learning module on the computer, values for the one or more parameters and the at least one explicit parameter to create an optimized model; creating, using the learning module on the computer, a trained model for each of the plurality of formulated tasks using an optimized value of the at least one explicit parameter and corresponding values of the one or more parameters; assigning, using a classification module on the computer, the at least one predefined label for each of the plurality of formulated tasks on to a live dataset based on the corresponding created trained model; and outputting, using the computer, signals corresponding to the live dataset assigned with the at least one predefined label for each of the plurality of formulated tasks.

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Claim 8:
8. The method of claim 1 , wherein the trained model has a predefined accuracy, which is inversely proportional to the complexity of the training dataset, wherein the complexity of the training dataset corresponds to at least one of size of the training dataset and a number of pre-labeled data sequences in the training dataset.