Abstract: A virtual agent with a dialogue management system and a method of training the dialogue management system is disclosed. The dialogue management system is trained using a deep reinforcement learning process. Training involves obtaining or simulating training dialogue data. During the training process, actions for the dialogue management system are selected using a Deep Q Network to process observations. The Deep Q Network is updated using a target function that includes a reward. The reward may be generated by considering one or more of the following metrics: task completion percentage, dialogue length, sentiment analysis of the user's response, emotional analysis of the user's state, explicit user feedback, and assessed quality of the action. The set of actions that the dialogue management system can take at any time may be limited by an action screener that predicts the subset of actions that the agent should consider for a given state of the system.

Abstract: A device may receive first information that identifies an input associated with a virtual agent application executing on a user device. The virtual agent application may provide an interface for a project involving a plurality of user devices. The device may determine, based on the first information that identifies the input, a first response based on second information. The device may determine, based on at least one of the first information that identifies the input or the first response and without user input, a second response. The device may provide, to the virtual agent application of the user device, fourth information that identifies at least one of the first response or the second response.

Abstract: The system and method generally include identifying whether an utterance spoken by a user (e.g., customer) is a complete or incomplete sentence. For example, the system may include a partial utterance detection module that determines whether an utterance spoken by a user is a partial utterance. The detection process may include providing a detection advice code that gives a recommendation for handling the utterance of interest. If it is determined that the utterance is an incomplete sentence, then the system and method can identify the type of utterance. For example, the system may include a partial utterance classification module that predicts the class of a partial utterance. The classification process may include providing a classification advice code that gives a recommendation for handling the utterance of interest.

Abstract: A system and method for spoken language understanding using recurrent neural networks (“RNNs”) is disclosed. The system and method jointly performs the following three functions when processing the word sequence of a user utterance: (1) classify a user's speech act into a dialogue act category, (2) identify a user's intent, and (3) extract semantic constituents from the word sequence. The system and method includes using a bidirectional RNN to convert a word sequence into a hidden state representation. By providing two different orderings of the word sequence, the bidirectional nature of the RNN improves the accuracy of performing the above-mentioned three functions. The system and method includes performing the three functions jointly. The system and method uses attention, which improves the efficiency and accuracy of the spoken language understanding system by focusing on certain parts of a word sequence. The three functions can be jointly trained, which increases efficiency.

Abstract: The system and method generally include identifying whether an utterance spoken by a user (e.g., customer) is a complete or incomplete sentence. For example, the system may include a partial utterance detection module that determines whether an utterance spoken by a user is a partial utterance. The detection process may include providing a detection advice code that gives a recommendation for handling the utterance of interest. If it is determined that the utterance is an incomplete sentence, then the system and method can identify the type of utterance. For example, the system may include a partial utterance classification module that predicts the class of a partial utterance. The classification process may include providing a classification advice code that gives a recommendation for handling the utterance of interest.

Abstract: A virtual agent with a dialogue management system and a method of training the dialogue management system is disclosed. The dialogue management system is trained using a deep reinforcement learning process. Training involves obtaining or simulating training dialogue data. During the training process, actions for the dialogue management system are selected using a Deep Q Network to process observations. The Deep Q Network is updated using a target function that includes a reward. The reward may be generated by considering one or more of the following metrics: task completion percentage, dialogue length, sentiment analysis of the user's response, emotional analysis of the user's state, explicit user feedback, and assessed quality of the action. The set of actions that the dialogue management system can take at any time may be limited by an action screener that predicts the subset of actions that the agent should consider for a given state of the system.

Abstract: A system and method for spoken language understanding using recurrent neural networks (“RNNs”) is disclosed. The system and method jointly performs the following three functions when processing the word sequence of a user utterance: (1) classify a user's speech act into a dialogue act category, (2) identify a user's intent, and (3) extract semantic constituents from the word sequence. The system and method includes using a bidirectional RNN to convert a word sequence into a hidden state representation. By providing two different orderings of the word sequence, the bidirectional nature of the RNN improves the accuracy of performing the above-mentioned three functions. The system and method includes performing the three functions jointly. The system and method uses attention, which improves the efficiency and accuracy of the spoken language understanding system by focusing on certain parts of a word sequence. The three functions can be jointly trained, which increases efficiency.

Abstract: A device may receive first information that identifies an input associated with a virtual agent application executing on a user device. The virtual agent application may provide an interface for a project involving a plurality of user devices. The device may determine, based on the first information that identifies the input, a first response based on second information. The device may determine, based on at least one of the first information that identifies the input or the first response and without user input, a second response. The device may provide, to the virtual agent application of the user device, fourth information that identifies at least one of the first response or the second response.

Abstract: A fused image of the person's hand is accessed, the fused image having been generated using a segmented graylevel image and a segmented color image. The hand in the fused image is identified. One or more finger tips and one or more finger valleys in the fused image are identified. One or more fingers of the hand are segmented, based on the identified finger tips and finger valleys. The one or more fingers of the hand are labeled. One or more features for each finger of the hand are determined.

Abstract: A fused image of the person's hand is accessed, the fused image having been generated using a segmented graylevel image and a segmented color image. The hand in the fused image is identified. One or more finger tips and one or more finger valleys in the fused image are identified. One or more fingers of the hand are segmented, based on the identified finger tips and finger valleys. The one or more fingers of the hand are labeled. One or more features for each finger of the hand are determined.

Abstract: A fused image of the person's hand is accessed, the fused image having been generated using a segmented graylevel image and a segmented color image. The hand in the fused image is identified. One or more finger tips and one or more finger valleys in the fused image are identified. One or more fingers of the hand are segmented, based on the identified finger tips and finger valleys. The one or more fingers of the hand are labeled. One or more features for each finger of the hand are determined.

Abstract: A fused image of the person's hand is accessed, the fused image having been generated using a segmented graylevel image and a segmented color image. The hand in the fused image is identified. One or more finger tips and one or more finger valleys in the fused image are identified. One or more fingers of the hand are segmented, based on the identified finger tips and finger valleys. The one or more fingers of the hand are labeled. One or more features for each finger of the hand are determined.