Abstract: Methods and systems are presented for providing an asynchronous communication system for facilitating computing services to computer nodes across multiple availability zones. Each computer node includes a client application configured to receive a service request and to generate a request file based on the service request. The client application uploads the request file to a folder hosted by a file sharing system and shared with a processing server. When a new request file is detected in the folder, the processing server downloads the request file from the file sharing system. Based on performing one or more computing services according to the request file, the processing server generates a response file including output data from the computing services. The processing server uploads the response file to the folder hosted by the file sharing system. The client application downloads the response file and presents the output data on a device.

Abstract: A system can access, from an interaction database, customer interaction information comprising a customer interaction, wherein the customer interaction information represents an interaction between a customer and an entity, and determine, based on a complaint identification model, whether a segment of the interaction comprises a complaint, wherein the complaint identification model has been generated based on machine learning applied to past customer interaction information representative of past customer interactions other than the customer interaction.

Abstract: There are provided systems and methods for named entity recognition in chat dialogues for customer relationship management systems. A service provider, such as an electronic transaction processor for digital transactions, may provide live chat service channels for assistance through live agents and chatbot services. When interacting with these channels, a user may engage in a chat dialogue with live agents. This may include lines of texts corresponding to the exchanged messages and may include named entities for particular types or categories of words that refer to a particular object or thing. To identify these named entities, a natural language processor may utilize machine learning and other engines for named entity recognition in customer relationship management systems to highlight the named entities in live service chats. Agents of the systems may view content that identify the named entities and interact with the named entities to view descriptions.

Abstract: There are provided systems and methods for sentence level dialogue summaries using unsupervised machine learning for keyword selection and scoring. A service provider, such as an electronic transaction processor for digital transactions, may provide live chat service channels for assistance through live agents and chatbot services. When interacting with these channels over a period of time, a user may create a dialogue with multiple different live agents and/or chatbots. These chat sessions may be asynchronous but have important and relevant information for future live agents to review. Thus, the service provider may provide a dialogue summarizer, which may summarize a dialogue automatically to a number of most relevant sentences. This may be done using an unsupervised machine learning system that utilizes different machine learning algorithms to select and rank keywords within the sentences. The sentences are then scored and highest scored sentences are output as a summary.