Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and applying a machine learning model. One of the methods includes the actions of obtaining a plurality of data points associated with a specified object; using a machine learning model to generate a prediction from the obtained plurality of data points, the prediction indicating a likelihood that the object will satisfy a particular parameter and a predicted scope for the parameter, wherein the machine learning model is trained using a training set comprising a collection of data points associated with a labeled set of objects, the label indicating the particular parameter and value for each object of the training set; and based on the prediction, classifying the specified object according to a determination of whether the predicted scope satisfies a threshold value.

Abstract: An apparatus, system, and method are disclosed for optimizing chat-based communications. A message module receives an outgoing message comprising a portion of a conversation between an agent and a user. An outgoing message may be generated in response to an incoming message from the user and received prior to sending the outgoing message to the user. An analysis module analyzes an incoming message and an outgoing message using a predefined machine learning model to identify one or more features of the outgoing message that have an influence on a desired outcome based on the incoming message. An action module generates one or more corrective actions related to the outgoing message based on one or more features that are identified using the machine learning model. One or more corrective actions are intended to increase the likelihood that an outgoing message will result in a desired outcome.

Abstract: A method for optimizing chat-based conversations using machine learning is disclosed, comprising the use of machine learning to improve the likelihood of the chat-based conversation attaining a long-term goal or maintaining the engagement/interest level of an entity engaged in the conversation.