Abstract: An interactive voice response system for interpreting sentiment of a conversation in real time is provided. The system may include a receiver. The receiver may be operable to receive a plurality of sentiment signals and a first portion of a conversation. The first portion of the conversation may include a plurality of interaction pairs. Each interaction pair may include an utterance and a system response. The system may include an utterance sentiment category determination module that may be operable to determine a sentiment category for each utterance. The system may include a sequential module. The sequential module may determine a conversation sentiment category based on the utterance sentiment categories and the order in which the utterance sentiment categories are received. The system may include an output module. The output module may output the conversation sentiment category. The conversation sentiment category may be used to determine a future system response.

Abstract: A device that is configured to establish a network connection between a user and an agent. The device is further configured to identify a first issue type for the user and to identify a first resolution type provided by the agent based on a conversation between the user and the agent. The device is further configured to identify a performance score from a resolution mapping based on a combination of the first issue type and the first resolution type. The device is further configured to identify a first knowledge area that is associated with the first issue type and to update a first knowledge score that is associated with the first knowledge area in a performance record for the agent based on the performance score. The device is further configured to send a recommendation to the agent based at least in part on the performance score.

Abstract: A device that is configured to assign users to an issue cluster based on issue types for the users. The device is further configured to identify available agents and to assign each available agent to one or more knowledge area clusters based on knowledge scores. A knowledge score indicates an expertise level for an agent in a knowledge area. The device is further configured to identify an issue cluster that is associated with an issue type and to identify a user from the issue cluster. The device is further configured to identify a knowledge area cluster that is associated with the issue type and to identify an agent from the knowledge area cluster. The device is further configured to establish a network connection between a user device associated with the user and a user device associated with the agent.

Abstract: Aspects of the disclosure relate to systems and methods for increasing the speed, accuracy, and efficiency of language processing systems. A provided method may include storing a plurality of modules in a database. The method may include configuring the plurality of modules in a multi-tier tree architecture. The method may include receiving an utterance. The method may include processing the utterance via a natural language processing (NLP) engine. The method may include routing the utterance. The routing may include identifying a highest tier module that matches a predetermined portion of the utterance. The method may include compiling a result set of modules. The method may include transmitting the result set of modules to the system user. The result set of modules may include a comprehensive and narrowly tailored response to the user request.

Abstract: Aspects of the disclosure relate to systems and methods for increasing the speed, accuracy, and efficiency of language processing systems. A provided method may include storing a plurality of modules in a database. The method may include configuring the plurality of modules in a multi-tier tree architecture. The method may include receiving an utterance. The method may include processing the utterance via a natural language processing (NLP) engine. The method may include routing the utterance. The routing may include identifying a highest tier module that matches a predetermined portion of the utterance. The method may include compiling a result set of modules. The method may include transmitting the result set of modules to the system user. The result set of modules may include a comprehensive and narrowly tailored response to the user request.

Abstract: Methods for leveraging a plurality of machine-learning algorithms to improve a chat interaction are provided. The methods may include monitoring for initiation of a live chat session; alerting and assigning a chat responder to the live chat session; engaging one or more of a plurality of automated chat tools, the tools loaded with artificial intelligence (AI), in order to improve the response of the responder during the session; reviewing and retrieving, using the AI, from a machine learning (ML) library in electronic communication with the AI, historical information; presenting, on a chat responder screen, selected actionable information generated based on the historical information, to the responder; integrating, based on pre-determined conditions, chat responses into the ML library; and integrating into the ML library, based on the same or other pre-determined conditions, chat comments. The chat comments are generated by a chat initiator.

Abstract: Apparatus and methods for interactive voice recognition. The apparatus and methods may include a canonical phrase derivation engine configured to derive canonical phrases from voice data. The apparatus may include an input engine configured to parse utterances. The apparatus may include a knowledge extraction engine to disambiguate the utterances into words, form a sequence from the words, extract context from the sequence, pair the sequence with a phrase of the canonical phrases, merge the sequence and the phrase to form a hybrid phrase, vectorize the hybrid phrase into a vector, and feed the vector into a non-linear classification engine to determine an intent corresponding to the utterances.

Abstract: An interactive voice response system for interpreting sentiment of a conversation in real time is provided. The system may include a receiver. The receiver may be operable to receive a plurality of sentiment signals and a first portion of a conversation. The first portion of the conversation may include a plurality of interaction pairs. Each interaction pair may include an utterance and a system response. The system may include an utterance sentiment category determination module that may be operable to determine a sentiment category for each utterance. The system may include a sequential module. The sequential module may determine a conversation sentiment category based on the utterance sentiment categories and the order in which the utterance sentiment categories are received. The system may include an output module. The output module may output the conversation sentiment category. The conversation sentiment category may be used to determine a future system response.

Abstract: A system for reducing computational load for training machine learning models is provided. The system may provide an end-to-end-solution for automating development, testing and updating of machine learning models in various operational environments. The system may determine which machine learning models included in a computer program product need to be retrained in response to a change in training data. For a computer program product that includes multiple models, the system only retrains target models, resulting in significant savings in computing resources. The system may also reduce the number of machine learning models that need to be generated for testing environments, further reducing consumption of computational resources.

Abstract: Aspects of the disclosure relate to systems and methods for increasing the speed, accuracy, and efficiency of language processing systems. A provided method may include storing a plurality of modules in a database. The method may include configuring the plurality of modules in a multi-tier tree architecture. The method may include receiving an utterance. The method may include processing the utterance via a natural language processing (NLP) engine. The method may include routing the utterance. The routing may include identifying a highest tier module that matches a predetermined portion of the utterance. The method may include compiling a result set of modules. The method may include transmitting the result set of modules to the system user. The result set of modules may include a comprehensive and narrowly tailored response to the user request.

Abstract: Aspects of the disclosure relate to systems and methods for increasing the speed, accuracy, and efficiency of language processing systems. A provided method may include storing a plurality of distinct rule sets in a database. Each of the rule sets may be associated with a different pipeline from a set of pipelines. The method may include receiving the utterance. The method may include tokenizing and/or annotating the utterance, determining a pipeline for the utterance, and comparing the utterance to the rule set that is associated with the pipeline. When a match is achieved between the utterance and the rule set, the method may include resolving the intent of the utterance based on the match. The method may include transmitting a request corresponding to the intent to a central server, receiving a response, and transmitting the response to the system user.

Abstract: A system for horizontal scaling of retraining machine learning models across operational domains is provided. The system may reduce computational overhead associated model retraining. The system may include an artificial intelligence (“AI”) engine that determines target machine learning models that need to be retrained in response to changed training data. The AI engine may assign daemons to the target models. The daemons may gather retraining requirements such as source code and training data. The daemons may schedule the target models for retraining on a CPU or a GPU based model training system.

Abstract: Methods for leveraging a plurality of machine-learning algorithms to improve a chat interaction are provided. The methods may include monitoring for initiation of a live chat session; alerting and assigning a chat responder to the live chat session; engaging one or more of a plurality of automated chat tools, the tools loaded with artificial intelligence (AI), in order to improve the response of the responder during the session; reviewing and retrieving, using the AI, from a machine learning (ML) library in electronic communication with the AI, historical information; presenting, on a chat responder screen, selected actionable information generated based on the historical information, to the responder; integrating, based on pre-determined conditions, chat responses into the ML library; and integrating into the ML library, based on the same or other pre-determined conditions, chat comments. The chat comments are generated by a chat initiator.

Abstract: When a caller initiates a conversation with an interactive voice response (“IVR”) system, the caller may be transferred to a live agent. Apparatus and methods are provided for integrating automated tools and artificial intelligence (“AI”) into the interaction with the IVR system. The automated tools and AI may track the conversation to decipher when to transfer the caller to the agent. The agent may determine which machine generated responses are appropriate for the caller. AI may be leveraged to suggest responses for both caller and agent while they are interacting with each other. The agent may transfer back the caller to the IVR system along with the appropriate machine generated response to maintain efficiency and shorten time of human agent interaction.

Abstract: Apparatus and methods for interactive voice recognition. The apparatus and methods may include a canonical phrase derivation engine configured to derive canonical phrases from voice data. The apparatus may include an input engine configured to parse utterances. The apparatus may include a knowledge extraction engine to disambiguate the utterances into words, form a sequence from the words, extract context from the sequence, pair the sequence with a phrase of the canonical phrases, merge the sequence and the phrase to form a hybrid phrase, vectorize the hybrid phrase into a vector, and feed the vector into a non-linear classification engine to determine an intent corresponding to the utterances.

Abstract: An interactive voice response system for interpreting sentiment of a conversation in real time is provided. The system may include a receiver. The receiver may be operable to receive a plurality of sentiment signals and a first portion of a conversation. The first portion of the conversation may include a plurality of interaction pairs. Each interaction pair may include an utterance and a system response. The system may include an utterance sentiment category determination module that may be operable to determine a sentiment category for each utterance. The system may include a sequential module. The sequential module may determine a conversation sentiment category based on the utterance sentiment categories and the order in which the utterance sentiment categories are received. The system may include an output module. The output module may output the conversation sentiment category. The conversation sentiment category may be used to determine a future system response.

Abstract: Methods for leveraging a plurality of machine-learning algorithms to improve a chat interaction are provided. The methods may include monitoring for initiation of a live chat session; alerting and assigning a chat responder to the live chat session; engaging one or more of a plurality of automated chat tools, the tools loaded with artificial intelligence (AI), in order to improve the response of the responder during the session; reviewing and retrieving, using the AI, from a machine learning (ML) library in electronic communication with the AI, historical information; presenting, on a chat responder screen, selected actionable information generated based on the historical information, to the responder; integrating, based on pre-determined conditions, chat responses into the ML library; and integrating into the ML library, based on the same or other pre-determined conditions, chat comments. The chat comments are generated by a chat initiator.

Abstract: When a caller initiates a conversation with an interactive voice response (“IVR”) system, the caller may be transferred to a live agent. Apparatus and methods are provided for integrating automated tools and artificial intelligence (“AI”) into the interaction with the IVR system. The automated tools and AI may track the conversation to decipher when to transfer the caller to the agent. The agent may determine which machine generated responses are appropriate for the caller. AI may be leveraged to suggest responses for both caller and agent while they are interacting with each other. The agent may transfer back the caller to the IVR system along with the appropriate machine generated response to maintain efficiency and shorten time of human agent interaction.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for providing a multi-channel cognitive resource platform configured for intelligent, proactive and responsive communication with a user, via a user device. The system is further configured to perform one or more user activities, in an integrated manner, within a single interface of the user device, without requiring the user to operate disparate applications. Furthermore, the system is configured to receive user input through multiple communication channels such as a textual communication channel and an audio communication channel. The system is further configured to switch between the various communication channels seamlessly, and in real-time.

Abstract: When a customer initiates an interaction with an interactive voice response (“IVR”) system, the customer may need to be transferred to a live agent. Apparatus and methods may formulate timing information for integrating a live agent into an interaction controlled by an artificial intelligence (“AI”) engine. The system may integrate machine generated responses into a customer interaction controlled by a live agent. The system may formulate timing information for intercepting the live agent with responses generated by the AI engine. The system may formulate the timing information using interactional analytics and preferences of a specific customer.