Ride-hailing apps, like Uber, have become known as safer alternatives to driving when intoxicated. Now the company is looking to learn a lot more about its late-night riders.
Uber filed a patent application Thursday, first spotted by CNN, that envisions using machine learning to detect whether a potential passenger is behaving abnormally. The patent application is vague about what exactly constitutes an “abnormal state,” but seems to be referring to intoxication and fatigue.
The system tracks user behavior based on typing speed and accuracy, device angle, walking speed and more. It then compares that data device angle, or walking speed. It plugs that information into an algorithm, along with additional information details about when and where the ride was requested.
If a request comes in late from an area packed with bars, and the user keeps dropping their phone, its a pretty good indicator that passenger is drunk.
When an altered user is identified, the application explains, there are a number of different options, including matching the user with specific drivers, alerting the driver about a user’s possible intoxication, and modifying pickup or drop-off locations. The application says the technology is a means to avoid “safety incidents and personal conflict incidents, [that] can occasionally occur when users and/or providers behave uncharacteristically.”
Uber has come under fire in the past few months after a CNN investigation found 103 Uber drivers have been accused of sexual assault or abuse within the past four years. In many cases, passengers were intoxicated. An Uber spokesperson in a statement to CNN said safety is the company’s top priority this year and cited recent protocol updates such as rerunning driver background checks annually moving forward.
The patent application was drafted by current or former members of Uber’s Trust & Safety team in 2016 and published Thursday. At the moment it is just a patent application and may never make it to Uber’s app.
Uber did not respond to a request for comment from CNBC.