Abstract: Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes identifying, by a computer processor, a dominant path of conversational behavior within conversation text data, wherein the dominant path comprises a plurality of path segment traversals between conversation turns in the conversation text data, and wherein the plurality of path segment traversals are accumulated into a weight for the one or more dominant paths; and modifying, by a computer processor, a digital conversation model in a computer-readable non-transitory memory device to contain the identified dominant path.

Abstract: Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes one or more methods of splitting transcribed conversations into groups related to a conversation ontology using metadata; identifying dominant paths of conversational behavior by counting the frequency of occurrences of the behavior for a given path; creating a conversation model comprising conversation behaviors, metadata, and dominant paths; and using the conversation model to assign a probability score for a matched input to the computer interlocutor or a generated output from the computer interlocutor.

Abstract: Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes one or more methods of splitting transcribed conversations into groups related to a conversation ontology using metadata; identifying dominant paths of conversational behavior by counting the frequency of occurrences of the behavior for a given path; creating a conversation model comprising conversation behaviors, metadata, and dominant paths; and using the conversation model to assign a probability score for a matched input to the computer interlocutor or a generated output from the computer interlocutor.

Abstract: A computer-generated visualization is created automatically in a format resembling a vertically-scrollable text-messaging user interface by segmenting the voice transcript into phrases, resolving how to indicate visually or to suppress periods of overlapping discussion (overtalk, interruption, etc.) by applying one or more rules, transformations, or both, and outputting the visualization onto a computer display device, into a printable or viewable report, or both.

Abstract: A system and method implemented on a computing device for analyzing a digital corpus of unstructured interlocutor conversations to discover intents, goals, or both intents and goals of one or more parties to the conversations, by grouping the conversation utterances according to semantic similarity clusters; selecting the best utterance(s) that mostly likely embody a party's stated goal or intent; creates a set of candidate intent names for each cluster based upon each intent utterance in each conversation in each cluster; rates each candidate intent (or goal) for each intent name; and selects the most likely candidate intent (or goal) name for the purposes of subsequent automation of future conversations such as, but not limited to, automated electronic responses using Artificial Intelligence and machine learning.

Abstract: A computer-implemented process for automatically managing computer-based conversation reason notations related to a digitally-recorded interlocutor conversation session, including automatically pre-selecting a reason notation from a set of allowable reason notations using artificial intelligence analysis. If a user selects another reason option, the records for the conversation and AI training data are updated accordingly.

Abstract: A computer-implemented method and computer system improvements for automatically generating a summary note related to a digitally-recorded interlocutor conversation, such as a chat transcript, including accessing a data corpus having at least one digitally-recorded conversation of text-based interlocutory conversation(s) including at least one labeled value, extracting at least one Conversation Feature from the labeled value(s), creating at least one Summary Feature from the extracted Conversation Feature(s), generating at least one Summary Note by combining one or more Narrative Structures with the Summary Feature(s), and digitally outputting the at least one Summary Note.

Abstract: Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes one or more methods of splitting transcribed conversations into groups related to a conversation ontology using metadata; identifying dominant paths of conversational behavior by counting the frequency of occurrences of the behavior for a given path; creating a conversation model comprising conversation behaviors, metadata, and dominant paths; and using the conversation model to assign a probability score for a matched input to the computer interlocutor or a generated output from the computer interlocutor.

Abstract: Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes one or more methods of splitting transcribed conversations into groups related to a conversation ontology using metadata; identifying dominant paths of conversational behavior by counting the frequency of occurrences of the behavior for a given path; creating a conversation model comprising conversation behaviors, metadata, and dominant paths; and using the conversation model to assign a probability score for a matched input to the computer interlocutor or a generated output from the computer interlocutor.

Abstract: A system and method implemented on a computing device for analyzing a digital corpus of unstructured interlocutor conversations to discover intents, goals, or both intents and goals of one or more parties to the conversations, by grouping the conversation utterances according to semantic similarity clusters; selecting the best utterance(s) that mostly likely embody a party's stated goal or intent; creates a set of candidate intent names for each cluster based upon each intent utterance in each conversation in each cluster; rates each candidate intent (or goal) for each intent name; and selects the most likely candidate intent (or goal) name for the purposes of subsequent automation of future conversations such as, but not limited to, automated electronic responses using Artificial Intelligence and machine learning.

Abstract: Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes one or more methods of splitting transcribed conversations into groups related to a conversation ontology using metadata; identifying dominant paths of conversational behavior by counting the frequency of occurrences of the behavior for a given path; creating a conversation model comprising conversation behaviors, metadata, and dominant paths; and using the conversation model to assign a probability score for a matched input to the computer interlocutor or a generated output from the computer interlocutor.

Abstract: A system and method implemented on a computing device for analyzing a digital corpus of unstructured interlocutor conversations to discover intents, goals, or both intents and goals of one or more parties to the conversations, by grouping the conversation utterances according to semantic similarity clusters; selecting the best utterance(s) that mostly likely embody a party's stated goal or intent; creates a set of candidate intent names for each cluster based upon each intent utterance in each conversation in each cluster; rates each candidate intent (or goal) for each intent name; and selects the most likely candidate intent (or goal) name for the purposes of subsequent automation of future conversations such as, but not limited to, automated electronic responses using Artificial Intelligence and machine learning.

Abstract: A system and method for visualizing, exploring and shaping conversational data within a corpus for selective export to train an artificial intelligence-based interlocutor platform, which includes selecting, according to user inputs, a plurality of the conversations for generation of an interactive radial space-filling sunburst-style diagram; responsive to user navigation controls, revising the sunburst diagram to allow provide selection, filtering and modification of the of the text-based interlocutory conversations; and, according to user export controls, formatting and saving at least a portion of the selected and filtered plurality of text-based interlocutory conversations into training data for an artificial intelligence-based automated interlocutor platform.

Abstract: A system and method implemented on a computing device for analyzing a digital corpus of unstructured interlocutor conversations to discover intents, goals, or both intents and goals of one or more parties to the conversations, by grouping the conversation utterances according to semantic similarity clusters; selecting the best utterance(s) that mostly likely embody a party's stated goal or intent; creates a set of candidate intent names for each cluster based upon each intent utterance in each conversation in each cluster; rates each candidate intent (or goal) for each intent name; and selects the most likely candidate intent (or goal) name for the purposes of subsequent automation of future conversations such as, but not limited to, automated electronic responses using Artificial Intelligence and machine learning.

Abstract: Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes one or more methods of splitting transcribed conversations into groups related to a conversation ontology using metadata; identifying dominant paths of conversational behavior by counting the frequency of occurrences of the behavior for a given path; creating a conversation model comprising conversation behaviors, metadata, and dominant paths; and using the conversation model to assign a probability score for a matched input to the computer interlocutor or a generated output from the computer interlocutor.

Abstract: Automated (autonomous) and computer-assisted preparation of initial training patterns for an Artificial Intelligence (AI) based automated conversational agent system, such as an AI-based chatbot, includes a computer processor accessing a corpus of digital weighted conversation models representing text-based interlocutory conversations, wherein each digital weighted conversation model contains annotations and paths, and wherein each path in each digital weighted conversation model is associated with a weight; selecting a plurality of the conversations which meet at least one criteria and in which at least one path meets at least one weight threshold according to the plurality of digital weighted conversation models; converting the weights associated with the selected conversations into initial training pattern values according to at least one Artificial Intelligence (AI) based automated conversational agent system; and exporting the training pattern values to at least one Artificial Intelligence (AI) based aut

Abstract: Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes one or more methods of splitting transcribed conversations into groups related to a conversation ontology using metadata; identifying dominant paths of conversational behavior by counting the frequency of occurrences of the behavior for a given path; creating a conversation model comprising conversation behaviors, metadata, and dominant paths; and using the conversation model to assign a probability score for a matched input to the computer interlocutor or a generated output from the computer interlocutor.

Abstract: A system and method implemented on a computing device for visualizing, exploring and examining various levels of supporting information details, by the computer accessing a corpus having a plurality of digital weighted conversation models of text-based interlocutory conversations, including annotations, paths, and weights; selecting a plurality of the conversations according to at least one annotation value at least one weight threshold; and generating a graphical user interface using the selected plurality of text-based interlocutory conversations, in which interlocutor Goals are depicted starting points in a flow-graph, interlocutor Turns are depicted as intermittent points in the flow-graph, and flow pipes are depicted between each interlocutor Goal and each Turn, to illustrate one or more text-based interlocutory conversations which traversed a flow from each goal to each Turn, in which each flow pipe is rendered with a width relative to other flow pipes in the flow graph according the weights.

Abstract: A system and method for visualizing, exploring and shaping conversational data within a corpus for selective export to train an artificial intelligence-based interlocutor platform, which includes selecting, according to user inputs, a plurality of the conversations for generation of an interactive radial space-filling sunburst-style diagram; responsive to user navigation controls, revising the sunburst diagram to allow provide selection, filtering and modification of the of the text-based interlocutory conversations; and, according to user export controls, formatting and saving at least a portion of the selected and filtered plurality of text-based interlocutory conversations into training data for an artificial intelligence-based automated interlocutor platform.

Abstract: A system and method implemented on a computing device for visualizing, exploring and examining various levels of supporting information details, by the computer accessing a corpus having a plurality of digital weighted conversation models of text-based interlocutory conversations, including annotations, paths, and weights; selecting a plurality of the conversations according to at least one annotation value at least one weight threshold; and generating a graphical user interface using the selected plurality of text-based interlocutory conversations, in which interlocutor Goals are depicted starting points in a flow-graph, interlocutor Turns are depicted as intermittent points in the flow-graph, and flow pipes are depicted between each interlocutor Goal and each Turn, to illustrate one or more text-based interlocutory conversations which traversed a flow from each goal to each Turn, in which each flow pipe is rendered with a width relative to other flow pipes in the flow graph according the weights.