AI & Automation - Incident Response Enterprise Mobile

UX/UI Design | September, 2021

My Role

User Research, Concepting, Definition & Ideation, Storyboarding, Wireframing, Prototyping, Pitching to Stakeholders

Team

UI Designer (Me)

Senior UX Designer

Tools

Figma, Photoshop, PowerPoint, MS Word

Timeframe

3 Months

About the project

This is a mobile app proposal I worked on as UI Designer in collaboration with a Senior UX Designer during my time at CentralSquare Technologies in September, 2021.

Prior to this project, CentralSquare did not offer a mobile app to customers, only a laptop application. This was a groundbreaking advancement for the company and opened up significant revenue streams.

Impact / Outcome

After researching and refining our concept, we pitched it to leadership, secured approval, and proceeded to create mockups and detailed designs.

The project was identified as a new mobile device innovation the company would invest in over the next several years. Some proof of concept work was completed and launched to customers in 2023. A wearable device with this type of functionality was also slated to begin in 2024.

Problem

Currently, police officers and investigators generate incident reports by taking notes while responding to incidents. Some incidents involve dangerous, high-speed chase situations, making it harder to collect accurate information in the moment.

They must complete detailed, time-consuming reports after each incident based on their notes and memory, which can lead to inconsistencies or missing details.

These reports are later used in courtrooms to determine a person’s innocence or guilt impacting citizens' lives and freedom, so accuracy in every detail is crucial.

On average, 40% of officers spend up to 4 hours per shift generating reports. Some officers report spending over half of their shift doing paperwork.

Solution

Use an AI driven mobile app to automate the incident response experience for police officers and investigators to make it easier for them to generate, analyze and report on the data they collected in the field when responding to incident calls.

AI could automatically collect contextual and data which could then be tagged and categorized to auto generate incident reports. Officers and staff could also establish relationships within police data and databases to assist in solving other crimes.

This would significantly lower the amount of time officers spend on report generation, reduce error frequency, improve data sharing opportunities across departments and agencies, increasing the number of solved cases.

Route of pursuit

This is a mockup of an officer responding to a report of a theft where the suspect has robbed someone and left the scene to a new location.

Points of interest

These are points of interest the officer identified on the way to the location. These could be crime scenes or witnesses they spoke to along the way which would need to be included in the incident report.

CCTV Feeds

This shows the location of local CCTV cameras that can be referenced for supporting video footage of the incident and response.

Benefits

This project proposed letting the technology officer’s already carry with them do the bulk of the work. Bluetooth enabled body cameras capture real-time contextual data (GPS, date, time, weather, road conditions) along with timestamped audio and video for indexing.

These details can be tedious / hard to capture or recall from memory. They also influence the outcomes of a significant amount of court cases.

Other benefits include:

Automatically capturing and pre-populating reports reduces time spent on paperwork, allowing officers to focus on safety and community engagement.
Syncing body cameras with a mobile app allows officers to pin key locations, helping reconstruct the incident timeline.
AI could assist in matching suspect photos to witness descriptions and linking evidence to reports.
Predictive AI could optimize officer routing for incidents with multiple responding units.

Incident response

The mockups below show how responding officers could use their mobile apps to determine routes of pursuit when responding to calls.

Prior to this project, officers would use third-party apps on their laptops for location routing. This meant multiple officers responding to the same call would not know where the other one was while in route — sometimes at very high speeds. This presented a life and death situation.

Officers currently relied on radio communication with dispatch while in pursuit to warn of any potential collisions. Real-time pursuit routing technology would solve this problem.

Storyboards

I sketched out some storyboards to help us explain our concept when presenting to leadership. Some of these represent features included in the P0 shipped product, some we expected would be included in future product releases.

Contextual data capture

Here, an officer exits his vehicle and his mobile app and bodycamera immediately start recording contextual data, date/time, weather, road conditions, etc.

We also envisioned a future wearable device that would track the officer’s heart rate, other vital signs and potentially mental state. According to an update from the CentralSquare Product Team, a wearable device is on their roadmap for 2023.

CCTV feeds

Concepting how a CCTV could be utilized to document an officer’s response to an incident.

Tagging video and audio footage for future indexing and documentation was a big part of this proposal. Currently, investigators spend inordinate amounts of time scrubbing through bodycam footage trying to piece together a case.

This project proposed using AI to cross-reference evidence data from multiple incident calls and investigative case data to try to suggest potential suspects who committed the crime. Auto tagging footage would save incredible amounts of staff time.

Geo tagging

Proposed feature for officers to be able to look up suspects using photographs and document their apprehension as evidence using their mobile phone camera and app.

Geo tagging an arrest location would also help detectives investigating and building a case to accurately process documentation without having to recreate it.

Currently investigators scrub through bodycam footage by hand to find significant response events and officer interactions.

Real-time dispatch display

During the entire incident response, a dispatcher would also be able to coordinate the responding officer’s locations using their geo-tagged location.

Another proposed feature is a real-time display for responding officers on foot to see where each other is by looking at their mobile device screen.

This is where a predictive AI would allow dispatchers to better organize responses and seeing how to route officers to an incident faster and potentially identifying officers that are better equipped to handle a specific situation.

Conclusion

The project was identified as a new mobile device innovation the company would invest in over the next several years. Some proof of concept work was completed and launched to customers in 2023. A wearable device with this type of functionality was also slated to begin in 2024.

This was a groundbreaking advancement for the company to build and offer a mobile app to customers which opened up significant revenue streams.