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: An improved data processing system that continuously analyzes and automates a process of identifying statistically significant patterns of customer behavior linked to a specific set of customer outcomes and presenting these visually in a graph with linkages to the root causes, customer events, each step in the customer behavior, and the customer outcome. The improved computing system provides a set of hypotheses and recommendations based on the pattern matching solutions in a computer database and allows the user of the system to simulate the anticipated outcomes.