Abstract: A system and method classify insurance documents. The system and method receive a request to access documents from an assessment queue stored in a memory of a device where each of the documents contain insurance data and have an associated destination and associated metadata. The system and method convert the set of documents encoded in a first file format to a second file format to remove the metadata. The system and method partition each document of the set of documents into separate stand-alone documents and converts each of the partitioned documents into separate recognition vectors. The system and method classifies the partitioned documents through an additive learning algorithm in which routing data is embedded in second metadata and merge the classified partitioned documents. The merged documents are routed to a remote destination independent of the originally intended destination and the associated metadata.

Abstract: A system and method simulate a chat-based conversation with a human user. The system and method receive a text message through the chat system that enables a receiver to transmit an automatically generated response in real time during a chat session. The system and method recognize characters in a text message and generates an automatic prediction or classification using a conversational assistant pod that resides in a container. The system and method generate an automatic response and modify computing resources by replacing the conversational assistant pod and the container with a second conversational assistant pod and a second container without interrupting the chat session.

Abstract: A system and method classify insurance documents. The system and method receive a request to access documents from an assessment queue stored in a memory of a device where each of the documents contain insurance data and have an associated destination and associated metadata. The system and method convert the set of documents encoded in a first file format to a second file format to remove the metadata. The system and method partition each document of the set of documents into separate stand-alone documents and converts each of the partitioned documents into separate recognition vectors. The system and method classifies the partitioned documents through an additive learning algorithm in which routing data is embedded in second metadata and merge the classified partitioned documents. The merged documents are routed to a remote destination independent of the originally intended destination and the associated metadata.

Abstract: A system and method simulate a chat-based conversation with a human user. The system and method receive a text message through the chat system that enables a receiver to transmit an automatically generated response in real time during a chat session. The system and method recognize characters in a text message and generates an automatic prediction or classification using a conversational assistant pod that resides in a container. The system and method generate an automatic response and modify computing resources by replacing the conversational assistant pod and the container with a second conversational assistant pod and a second container without interrupting the chat session.