International business machines corporation (20240177030). IDENTIFYING UNKOWN DECISION MAKING FACTORS FROM COMMUNICATIONS DATA simplified abstract

IDENTIFYING UNKOWN DECISION MAKING FACTORS FROM COMMUNICATIONS DATA

Organization Name

international business machines corporation

Inventor(s)

Matthias Graefe of Eppstein (DE)

Daniel Thomas of Raleigh NC (US)

Zachary A. Silverstein of Georgetown TX (US)

Jacob Ryan Jepperson of St. Paul MN (US)

IDENTIFYING UNKOWN DECISION MAKING FACTORS FROM COMMUNICATIONS DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240177030 titled 'IDENTIFYING UNKOWN DECISION MAKING FACTORS FROM COMMUNICATIONS DATA

Simplified Explanation

The patent application describes a process mining system and method for automatically identifying unknown decision-making factors in a process model. Here is a simplified explanation of the abstract:

• Access a process model with steps in a process, including decision-making points.
• Obtain electronic communication data for human participants in the process.
• Analyze the data to identify decision-making content.
• Input the content to a machine learning model to predict a decision-making factor.
• Update the process model based on the predicted factor.

Potential Applications: - Business process optimization - Workflow automation - Decision support systems

Problems Solved: - Identifying unknown decision-making factors - Improving process efficiency - Enhancing decision-making processes

Benefits: - Increased process accuracy - Enhanced decision-making capabilities - Streamlined workflows

Potential Commercial Applications: - Enterprise software solutions - Consulting services for process improvement - Decision support tools

Possible Prior Art: - Existing process mining systems - Machine learning models for decision-making analysis

Unanswered Questions:

1. How does the system handle complex decision-making scenarios with multiple factors influencing the outcome? 2. What are the limitations of the system in terms of scalability to large and complex processes?

Original Abstract Submitted

process mining systems and methods are provided for automatically identifying unknown decision making factors. in implementations, a computer-based method includes accessing a process model comprising a representation of steps in a lifecycle of a process, including a process step associated with a decision making point of the process, wherein a decision input at the decision making point determines a next step in the process from multiple next-step options; obtaining electronic communication data for communications between human participants in the process; analyzing the electronic communication data to identify decision making content associated with the decision making point; inputting the decision making content to a trained machine learning (ml) model, thereby generating an output of a decision making factor predicted to impact the decision input at the decision making point; automatically updating the process model based on the predicted decision making factor, thereby generating an updated process model.