Abstract: According to some embodiments, a system and method comprising a plurality of automation tools; and a shared automation module, coupled to the plurality of automation tools including: a computer processor; a computer memory, coupled to the computer processor, storing instructions that, when executed by the computer processor cause the shared automation module to: receive a selection of a first automation tool of the plurality of automation tools; receive a selection of a second automation tool of the plurality of automation tools; execute the first automation tool to generate a first automation tool output; and execute the second automation tool using the stored first automation tool output to generate a second automation tool output. Numerous other aspects are provided.

Abstract: A significant correlation framework is provided herein for translating input commands to intents. The input commands may be natural language commands, received from a variety of input channels, which may be translated to intents or other runtime-bindable execution objects. The significant correlation framework may use interpreter nodes for translating the input commands by calculating the strength of correlation between an input command and an intent. The significant correlation framework may analyze the sequence of intents or the timing of translated intents to enhance the accuracy of the translation. The significant correlation framework may maintain a history of command translations, and may compare current translations against the history to improve accuracy of the translations. The significant correlation framework may switch between a depth-first mapping method and a breadth-first mapping method. Depth-first mapping may translate commands through a single interpreter node.

Abstract: A significant correlation framework is provided herein for translating input commands to intents. The input commands may be natural language commands, received from a variety of input channels, which may be translated to intents or other runtime-bindable execution objects. The significant correlation framework may use interpreter nodes for translating the input commands by calculating the strength of correlation between an input command and an intent. The significant correlation framework may analyze the sequence of intents or the timing of translated intents to enhance the accuracy of the translation. The significant correlation framework may maintain a history of command translations, and may compare current translations against the history to improve accuracy of the translations. The significant correlation framework may switch between a depth-first mapping method and a breadth-first mapping method. Depth-first mapping may translate commands through a single interpreter node.