Abstract: Systems, apparatuses, and methods for providing a more effective customer service support system. A trained model may be created or generated by a process that includes acquiring a corpus of documents (such as customer generated messages), generating keyphrases from each document in the corpus of documents, generating word embeddings from each document in the corpus of documents, performing a clustering operation on the word embeddings, assigning each document in the corpus to a duster and labeling each duster, and training a machine learning or text classification model using the most relevant documents in a cluster and the label assigned to that cluster. The methods include a processing flow that provides multiple ways for a customer to navigate and select their desired intent or purpose in seeking customer assistance. As part of the processing flow, the trained machine learning model and intent tree data are provided to a customer seeking assistance.

Abstract: Systems, apparatuses, and methods for providing a more effective customer service support system. In some embodiments, the systems, apparatuses, and methods include a user interface (UI) and process flow for implementing a conversation-based customer service support system. The user interface and associated processing enable a user/customer to quickly and accurately select an intent, goal, or purpose of their customer service request. In some embodiments, this is achieved by a set of user interface displays or screens and an underlying logic that provide a user with multiple ways of selecting, identifying, or describing the intent, goal, or purpose of their request for service or assistance.

Abstract: Systems, apparatuses, and methods for the interpretation and routing of short text messages, such as those that might be received as part of a “chat” between a customer and a customer service representative. In some embodiments, this is achieved by constructing word “vectors” based on the text in a message, with a token corresponding to each word. The word vectors are then compared to a set of mutually orthogonal unit vectors representing the “classes” or “categories” of messages that are received and are intended to be acted upon by a person or automated process. The orthogonal class unit vectors are generated by training a machine learning model using a set of previously classified text or messages.

Abstract: Systems, apparatuses, and methods for the interpretation and routing of short text messages, such as those that might be received as part of a “chat” between a customer and a customer service representative. In some embodiments, this is achieved by constructing word “vectors” based on the text in a message, with a token corresponding to each word. The word vectors are then compared to a set of mutually orthogonal unit vectors representing the “classes” or “categories” of messages that are received and are intended to be acted upon by a person or automated process. The orthogonal class unit vectors are generated by training a machine learning model using a set of previously classified text or messages.