SaverOne 2014 Ltd. (NASDAQ: SVRE) shares experienced a significant turnaround during the after-market trading session last Friday, rising 9.21% to close at $2.37. This rebound helped offset a regular-session decline of -7.26%, when the stock ended the day at $2.17. The recovery in SVRE’s share price is being attributed to investor optimism following a newly granted patent by the United States Patent and Trademark Office (USPTO).
New Patent Targets Safer Mobile Device Use in Vehicles
SaverOne last week unveiled that it has been awarded U.S. Patent No. 12,326,512, which is entitled to “System and Method for Classifying a Mode of Operation of a Mobile Communication Device in a Volume Based on Sensor Fusion.” By identifying where and how a mobile device is being used within a moving car, the patented technique aims to improve in-cabin safety.
The invention makes use of SaverOne’s in-house Phone position Unit (PLU), which uses radio frequency (RF)-based tracking to pinpoint a mobile device’s exact position. To categorize the mobile device’s operating mode—such as idle, calling, texting, or streaming—this data is then combined with input from non-RF sensors. Crucially, it enables the system to discern if the driver or a passenger is using the device.
Strengthening an Expanding Intellectual Property Portfolio
With the addition of this latest U.S. patent, SaverOne now holds a total of 23 patents—14 of which have been granted across major regions, including the U.S., U.K., Europe, Israel, and China. The remaining nine applications are still pending. The company’s growing IP portfolio underpins its mission of bringing its safety-enhancing solutions to international markets.
Positioning for Global Commercialization and Road Safety Leadership
The granted patent is protected for 20 years from the date of effective filing under current U.S. patent law. This development is seen by SaverOne as a significant turning point in its plan to transform car safety systems. The business keeps creating strong solutions to lower the number of distracted driving accidents by using state-of-the-art sensor fusion and real-time mobile activity categorization.
