International Business Machines Corporation

Mycybertwin Group Pty Ltd

John Zakos

David Nahamoo

Raimo Bakis

Lazaros Polymenakos

Liesl Jane Beilby

Ladislav Kunc

Liesl Jane Capper

Glenys Anne Westwood McLaughlin

Cheng Wu

Glenys Anne McLaughlin

Embodiments provide a computer implemented method, in a data processing system including a processor and a memory including instructions which are executed by the processor to cause the processor to train an enhanced chatflow system, the method including: ingesting a corpus of information including at least one user input node corresponding to a user question and at least one variation for each user input node; for each user input node: designating the node as a class; storing the node in a dialog node repository; designating each of the at least one variations as training examples for the designated class; converting the classes and the training examples into feature vector representations; training one or more training classifiers using the one or more feature vector representations of the classes; and training classification objectives using the one or more feature vector representations of the training examples.

System and method for enhanced chatflow application

A system and method performs automated domain concept discovery and clustering using word embeddings by receiving a set of documents for natural language processing for a domain, representing a plurality of entries in the set of documents as continuous vectors in a high dimensional continuous space, applying a clustering algorithm based on a mutual information optimization criterion to form a set of clusters, associating each entry of the plurality of entries with each cluster in the set of clusters through formalizing an evidence based model of each cluster given each entry, calculating a mutual information metric between each entry and each cluster using the evidence based model, and identifying a nominal center of each cluster by maximizing the mutual information.

Domain concept discovery and clustering using word embedding in dialogue design

Embodiments provide a computer implemented method of training an enhanced chatflow system, comprising: ingesting a corpus of information comprising at least one user input node corresponding to a user question and at least one expert-designed variation for each user input node; matching one or more user inputs to one or more corresponding dialog nodes using regular expressions and delimiters; ingesting one or more usage logs from a deployed dialog system, each usage log comprising at least one user input node; for each user input node: designating the node as a class; storing the node in a dialog node repository; designating each of the at least one variations as training examples for the designated class; converting the classes and the training examples into feature vector representations; training one or more classifiers and one or more classification objectives using the feature vector representations.

In one aspect, the invention concerns a method of operating a chatbot to engage in a conversation with a correspondent the method comprises building a profile having plural profile variables for the correspondent; and during the conversation with the correspondent; selecting a node in the conversation data structure for processing based on the one or more profile variables, and processing the node to follow a conversation path based on the node's coded instructions and/or relationship with other nodes. In another aspect, the method comprises the steps of attempting to match the received input message with a node in the conversation data structure of the chatbot; if a matched node is found, selecting the matched node for processing; but if a matched node is not found, selecting a node for processing using a fuzzy search, or using a default procedure.

Chatbots

In one aspect, the invention concerns a method of operating a chatbot to engage in a conversation with a correspondent the method comprises building a profile having plural profile variables for the correspondent; and during the conversation with the correspondent; selecting a node in the conversation data structure for processing based on the one or more profile variables, and processing the node to follow a conversation path based on the node's coded instructions and/or relationship with other nodes. In another aspect, the method comprises the steps of attempting to match the conversation data structure of the chatbot; if a matched node is found, selecting the matched node for processing; but if a matched node is not found, selecting a node for processing using a fuzzy search, or using a default procedure.

The invention concerns cyberpersonalities, including their and varied use in artificial reality. A cyberpersonality is comprised of a base personality (). The base personality () is selected from a set of base personalities, each one representing the personality of a theoretical person. The cyberpersonality also includes a dynamic personality () that reflects the actual person (real or company) that the cyberpersonality is meant to represent and is able to learn. Information contained in the base () and dynamic () personality can be used to allow the person that the cyberpersonality mimics to interact in the artificial reality without direct control. The cybersonality can chat with third parties, including asking questions and answering questions, so as to learn more about each other. Other uses are related to searching, advertising and direct marketing.

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Ashmore, Australia

John Zakos