Sap Se

Deepanshi Katoch

Aavishkar Bharara

Ankita Prabhu

Anil Rao Arun

Ramya K S

Shrijan Shrivastav

Anbarasu Ayyasami

Ashwani Kumar Luhaniwal

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. Breadth-first mapping may translate commands using multiple interpreter nodes.

Significant correlation framework for command translation

Growing community of inventors

Bhopal, India

Deepanshi Katoch