Abstract: Methods and apparatuses are described for dialog control flow in information retrieval applications. A server establishes a chat-based communication session between an information retrieval application and a client device. The server determines a user intent from utterances received from a user of the client device and initiates a first dialog workflow associated with the user intent. The server invokes NLP services using the utterances to determine a comprehension score for the user intent and identifies a first one of the NLP services to continue the first dialog workflow when the comprehension score is at or above a threshold value, including generating a response to the utterances using the first NLP service. The server delegates the communication session to a second dialog workflow when the comprehension score is below the threshold value, including invoking a generalized language processing service associated with the second dialog workflow using the user intent to generate a response to the utterances.

Abstract: A computer implemented method is provided for assisting an agent in providing feedback to a customer during a chat session. The method includes receiving live feedback from the agent to the customer in response to an inquiry from the customer. The method also includes determining if at least one automated response is needed by comparing and matching the live feedback with at least one predefined trigger pattern. The method further includes identifying one or more trigger phrases in the live feedback if automated response is needed and determining from a conversation history database at least one automated response based on the one or more trigger phrases. The method additionally includes appending the at least one automated response to the live feedback for presentation to the customer during the chat session.

Abstract: Methods and apparatuses are described for intent-aware virtual assistant chat routing. A server receives a request from a user of a client device to transfer a chat-based communication session from a virtual assistant application to an agent. The server captures a topic value and a user message from the chat-based session. The server identifies a user intent associated with the user message by applying a trained intent classification model. When a user intent is identified, the server selects an agent to receive the chat-based session based upon the user intent and a complexity value associated with the user intent and connects an agent device associated with the selected agent to the chat-based session. When a user intent is not identified, the server selects an agent to receive the chat-based session based upon the topic value and connects an agent computing device associated with the selected agent to the chat-based session.

Abstract: Methods and apparatuses are described for capturing and utilizing cross-channel context data in conversation service application communication sessions. A computing device establishes a first communication session between a first conversation service application and a first remote device. The orchestration utility detects an intent to transfer the first communication session to a second remote device. The orchestration utility captures context data generated during the first communication session. The computing device establishes a second communication session between a second conversation service application and the second remote computing device. The orchestration utility configures the second communication session based upon the context data, including synchronizing a state of the second communication session to a state of the first communication session.

Abstract: Systems and methods for switching between natural language processing units during an interaction using a dialog delegation processor. The method includes receiving a dialog utterance from a user via a user device. The method further includes determining a general intent based on the dialog utterance using a general natural language processor. The method also includes determining at least one specific intent corresponding to the general intent using the general natural language processor. The method also includes selecting a specific natural language processor corresponding to the at least one specific intent. The method further includes determining a dialog response corresponding to the at least one of specific intent using the specific natural language processor. The method also includes generating for display the dialog response on the user device.

Abstract: Methods and apparatuses are described for intelligent dialogue recovery in virtual assistant communication sessions. A chat session is established between a virtual assistant (VA) and a remote device. A primary dialogue is created between the VA and the remote device, including (i) requesting from a first natural language processing (NLP) service an intent associated with user messages and (ii) waiting for a corresponding response. A secondary dialogue is created between the VA and the remote device that is synchronized to a state of the primary dialogue. When the response from the first NLP service succeeds, primary messages are generated and transmitted to the remote device in the primary dialogue. When the response from the first NLP service fails, an intent is requested from the second NLP service, and secondary messages are generated and transmitted to the remote device in the secondary dialogue.

Abstract: Methods and apparatuses are described for dynamic option reselection in virtual assistant (VA) communication sessions. A chat session is established between a VA application and a remote computing device. The VA application captures a first conversation intent generated by a user of the remote device, where the first conversation intent is part of a first workflow. The VA application generates an option selection feature based upon the first conversation intent, comprising a plurality of options and associated with a reference identifier. The VA application receives a selection of a first option in the option selection feature from the remote device. The VA application initiates a second workflow in response to the selected. The VA application detects a selection of a second option in the option selection feature from the remote device and restores the chat session to a state in the first workflow using the reference identifier.

Abstract: Methods and apparatuses are described for dynamic option reselection in virtual assistant (VA) communication sessions. A chat session is established between a VA application and a remote computing device. The VA application captures a first conversation intent generated by a user of the remote device, where the first conversation intent is part of a first workflow. The VA application generates an option selection feature based upon the first conversation intent, comprising a plurality of options and associated with a reference identifier. The VA application receives a selection of a first option in the option selection feature from the remote device. The VA application initiates a second workflow in response to the selected. The VA application detects a selection of a second option in the option selection feature from the remote device and restores the chat session to a state in the first workflow using the reference identifier.

Abstract: Methods and apparatuses are described for capturing and utilizing context data in virtual assistant communication sessions. A chat-based communication session is established between a virtual assistant application and a remote device. An event manager captures a first data event of the virtual assistant application. A parent software application receives the first data event and selects one or more session context data elements using a callback function of the first data event. The event manager captures a second data event of the parent software application comprising the session context data elements. The virtual assistant receives the second data event and inserts one or more messages comprising the session context data elements from the second data event into the chat-based communication session.

Abstract: Methods and apparatuses are described for session-based data storage for chat-based communication sessions. A computing device connects to a data storage area associated with an active chat-based communication session, the data storage area comprising data storage slots. The computing device identifies a first data event during the chat-based communication session, the first data event comprising one of a data capture event, a data retrieval event, or a data prefill event. The computing device identifies a data storage slot in the data storage area corresponding to the first data event, the data storage slot comprising a slot name and a slot value. The computing device selects data elements associated with the chat-based communication session for insertion into the data storage slot based upon the event type of the first data event. The computing device stores the selected data elements in the data storage slot for retrieval during the chat-based communication session.

Abstract: Systems and methods for switching between natural language processing units during an interaction using a dialog delegation processor. The method includes receiving a dialog utterance from a user via a user device. The method further includes determining a general intent based on the dialog utterance using a general natural language processor. The method also includes determining at least one specific intent corresponding to the general intent using the general natural language processor. The method also includes selecting a specific natural language processor corresponding to the at least one specific intent. The method further includes determining a dialog response corresponding to the at least one of specific intent using the specific natural language processor. The method also includes generating for display the dialog response on the user device.