Abstract: A system for dynamic semantic role classification, through an entity's natural language process (NLP) pipeline is provided. The system may include assigning semantic role classifiers to tokens included in utterances received from user nodes. The system may include using a machine learning algorithm to assign the semantic role classifiers. The machine learning algorithm may assign the semantic role classifiers based on a calculated correlation value. The machine learning algorithm may use training and testing data sets to dynamically update the semantic role classifiers.

Abstract: Apparatus, methods and systems for an utterance parsing system is provided. The system may include a receiver, a tokenizer, a semantic role classifier, an action topic ontology and a semantic relationship builder. The receiver may receive an utterance. The tokenizer may tokenize the utterance into tokens. The semantic role classifier may determine which tokens are candidates for semantic role classification. The semantic role classifier may identify a semantic role for each candidate token. The semantic role classifier may determine that one semantic role is an action, and that one semantic role is a topic. The semantic role classifier may determine a class that corresponds to the topic-token, and a class that corresponds to the action-token. The action topic ontology may store the classes and one or more relationships between the classes. The semantic relationship builder may determine a vector defining a relationship between the action-token and the topic-token.

Abstract: Apparatus, methods and systems for contextual prediction processing is provided. Methods may include receiving a conversation from an entity. The conversation may include current utterance, previous utterances and details. Methods may include using an action-topic ontology to build, using data retrieved from the current utterance, a conversation frame that corresponds to the current utterance. Methods may include merging the conversation frame with data, retrieved from the previous utterances and the details, to generate a target conversation frame. Methods may include validating the target conversation frame to prevent looping over historic data in the event that the current utterance fails to add relevant information. Methods may include generating an enhanced contextual utterance based on algorithms and the target conversation frame. The enhanced contextual utterance may be used to understand the current utterance in a context of the conversation.

Abstract: A method for utilizing a dual-pipeline utterance output construct to obtain an output corresponding to an utterance of a user is provided. The method includes receiving the utterance, transmitting the utterance through a non-contextual pipeline to determine a first output prediction, transmitting the utterance through a contextual pipeline to determine a second output prediction, transmitting the first output prediction and the second output prediction to a decider to formulate a final prediction of the user's input, constructing a response to the utterance based on the final prediction; and executing the response to the utterance.

Abstract: A method for reducing response time of an utterance received in a multilingual chatbot system may be provided. The utterance may be received in a language other than a default language with respect to the multilingual chatbot system. The method may include receiving a request utterance from a remote user device and determining that the request utterance is not in the default language. In response to the determining, the method may include transmitting the request utterance, in parallel, to both a translational track for translating the request utterance and an intelligence track for translating the request utterance. The method may include computing a first intent of the translated request utterance at the translational track. The method may include computing a second intent based on a ML request utterance. The method may include determining that the first intent matches the second intent and following the determining, generating a response utterance.

Abstract: Systems are provided for building semantic frames. Systems may include building a semantic frame using a machine learning algorithm. The algorithm may identify: an index number of a token, a semantic role classifier assigned to the token, a corresponding correlation value and an index number of one or more related tokens. The algorithm may also create a semantic frame using the identified information. Systems may include building semantic frames for multiple tokens within an utterance. Systems may include building semantic frames for a plurality of tokens within a plurality of utterances. The plurality of utterances may be components of a conversation. Systems may also include summarizing the conversation using the semantic frames.

Abstract: Systems, methods, and apparatus are provided for integrating AI-powered bot-generated responses with an agent interface during a live session with a customer. In response to a customer request, a live chat session may be initiated with an agent at first platform that includes an agent interface. A parallel session may be initiated at a second platform that includes an interactive response system and AI engine. An input from a customer may be displayed at the first platform and may also be received at the second platform. The second platform may derive intent from the input and generate an AI-based response. The response may be displayed in a window at the first platform. The agent may approve, reject, or modify the generated response. Following agent approval, the response may be inserted into the live customer session.

Abstract: A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.

Abstract: A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.

Abstract: A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.