Quantifying Police Interactions (QPI)

Title: Quantifying Police Interactions (QPI)


Unmet Need: Underutilization and restriction to the Body Worn Cameras (BWC) hampers review rates, hindering intelligence gatherings and evidence identification.

The limited access to BWC footage, with existing software only permitting officers to view it, has resulted in less than 5% of recordings being reviewed by police agencies. The underutilization hinders the potential of BWC footage to gather intelligence and serve as valuable evidence in discovering important information.

A team of Inventors has developed software named QPI (Quantifying Police Interactions), a web-App that is capable of viewing and labeling objectively police BWC footage, utilizing both manually and automatically. This software collects, analyzes, and displays data to facilitate department reflection focusing on service oriented rather than prioritizing profit. 


The Technology: Improved performance of web application by increasing the level of efficiency at gathering data from incidents.

The invention developed a web application that assists police departments providing evidentiary value, identifying strengths and weakness within the agency. This user interface allows for quick labeling of events that take place during recorded police BWC footage.



  • Enhanced evidence management and organization
  • Improved training tool for police officers and department reflection
  • Promote public trust and community relations by demonstrating unbiased approach
  • Potentially improve local, state, and federal laws


  • Speeds up the user’s workflow
  • Access to the most modern tools
  • Maintainability for future teams
  • Seamless deployment of new versions
  • Integration with existing systems

Patent Information:

Provisional patent application has been filed.

Learn More

Scott Steiger
Associate Director
Washington State University
(509) 335-7065
Reference No: TECH-23/3492


David Makin
Dale Willits
Mantz Wyrick
Peyton Urquhart
Lucas Da Silva
Zachary Barnett
Megan Parks

Key Words

Machine Learning
Risk Monitoring