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Uber wants to know how drunk its passengers are

The ride-hailing service may be looking at ways to tailor its app depending on how much a user has had to drink.

A new patent application suggests Uber is looking to use machine learning to gauge the sobriety of its users.

The application uses fairly broad terminology, but specifies the aim of predicting “uncharacteristic user states” through the analysis of customer data. 

By using a phone’s gyrometers and location data, Uber could hypothetically track how fast you were moving or how upright you were standing, while a measure of a user’s “data input accuracy” could draw conclusions from how hard your smartphone’s autocorrect was working.

This information would allow Uber to further personalise its service, ensuring pick-up points were in well-lit areas, or even presenting a simpler version of the map interface on its app.

No YOU'RE drunk

Not everyone is likely to welcome the prospect of Uber harvesting yet more of its customer’s data, however. Uber has come under fire before for its misuse of customer data, which included Uber employees being able to track the location of ex-partners over a centralised ‘God View’ tool.

While Uber is attempting to rehabilitate its public image, users may be wary of an algorithm that can tell how many cocktails they’ve downed on a night out. And while the data could help drivers cater to user needs – maybe with a pre-reclined seat, or a helpfully placed bucket – it could also pave the way for further breaches of trust.

Henry St Leger

Henry is TechRadar's News & Features Editor, covering the stories of the day with verve, moxie, and aplomb. He's spent the past three years reporting on TVs, projectors and smart speakers as well as gaming and VR – including a stint as the website's Home Cinema Editor – and has been interviewed live on both BBC World News and Channel News Asia, discussing the future of transport and 4K resolution televisions respectively. As a graduate of English Literature and persistent theatre enthusiast, he'll usually be found forcing Shakespeare puns into his technology articles, which he thinks is what the Bard would have wanted. Bylines include Edge, T3, and Little White Lies.