18071965. IDENTIFYING UNKOWN DECISION MAKING FACTORS FROM COMMUNICATIONS DATA simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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 18071965 titled 'IDENTIFYING UNKOWN DECISION MAKING FACTORS FROM COMMUNICATIONS DATA

Simplified Explanation

The abstract describes a patent application for a process mining system that automatically identifies unknown decision-making factors in a process by analyzing electronic communication data and using machine learning models to predict decision-making factors that impact the process.

• The system accesses a process model with decision-making points.
• It obtains electronic communication data between participants in the process.
• The data is analyzed to identify decision-making content.
• A machine learning model is used to predict decision-making factors.
• The process model is updated based on the predicted factors.

Potential Applications

This technology could be applied in various industries such as manufacturing, healthcare, finance, and logistics to improve decision-making processes and optimize workflows.

Problems Solved

This technology helps in identifying unknown decision-making factors that may impact the process, leading to more informed decision-making and improved efficiency.

Benefits

The system automates the process of identifying decision-making factors, saving time and resources. It also helps in improving the overall performance and effectiveness of the process.

Potential Commercial Applications

"Automated Decision-Making Factor Identification System for Process Optimization"

Possible Prior Art

One possible prior art could be traditional process mining systems that focus on analyzing process data to improve efficiency and performance. However, the use of machine learning models to predict decision-making factors based on electronic communication data is a novel approach.

Unanswered Questions

How does the system ensure the accuracy and reliability of the predicted decision-making factors?

The system may need to be validated against known decision-making factors to ensure the accuracy and reliability of the predictions.

What are the potential limitations or challenges of implementing this technology in real-world scenarios?

There may be challenges related to data privacy, data quality, and integration with existing systems when implementing this technology in real-world scenarios.

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.