System and method for automated structure retrieval of complex standard operating procedures

A system for automated structure retrieval of complex standard operating procedures (SOPs), the system comprising:

• a processor;
• and
• a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to:
• implement a model leveraging a Large Language model (LLM) which receives, as input, an original SOP document, wherein the LLM breaks the original SOP document into a plurality of task segments;
• identify starting and ending sentences of each task segment of said plurality of task segments by utilizing the LLM and thereby detecting context shifts and detecting boundaries between different tasks;
• deterministically extract, in response to identifying, text from the original SOP document that falls within the detected boundaries thereby capturing information from the original SOP document within the task segments by leveraging the LLM's natural language understanding capabilities;
• transform, in response to deterministically extracting, the task segments into a Directed Acyclic Graph (DAG) that includes a series of subtasks to keep track of dependencies between SOP tasks;
• validate the DAG by evaluating its attributes including:
• dependency accuracy, dependency output alignment, DAG connectivity, input accuracy, and output accuracy;
• and
• automatically output, in response to validating, a structured SOP document corresponding to the DAG, confirms that the dependencies in the subtasks are correctly captured, and confirms an alignment between initial and goal states of the structured SOP document and the original SOP document.

A non-transitory computer readable medium configured to store instructions for automated structure retrieval of complex standard operating procedures (SOPs), the instructions, when executed, cause a processor to perform the following:

• implementing a model leveraging a Large Language model (LLM) which receives, as input, an original SOP document, wherein the LLM breaks the original SOP document into a plurality of task segments;
• identifying starting and ending sentences of each task segment of said plurality of task segments by utilizing the LLM and thereby detecting context shifts and detecting boundaries between different tasks;
• deterministically extracting, in response to identifying, text from the original SOP document that falls within the detected boundaries thereby capturing information from the original SOP document within the task segments by leveraging the LLM's natural language understanding capabilities;
• transforming, in response to deterministically extracting, the task segments into a Directed Acyclic Graph (DAG) that includes a series of subtasks to keep track of dependencies between SOP tasks;
• validating the DAG by evaluating its attributes including:
• dependency accuracy, dependency output alignment, DAG connectivity, input accuracy, and output accuracy;
• and
• automatically outputting, in response to validating, a structured SOP document that confirms connectivity of the structured SOP document corresponding to the DAG, confirms that the dependencies in the subtasks are correctly captured, and confirms an alignment between initial and goal states of the structured SOP document and the original SOP document.