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Cannabix Technologies Inc C.BLO

Alternate Symbol(s):  BLOZF

Cannabix Technologies Inc. is a Canada-based developer of marijuana and alcohol breathalyzer technologies for law enforcement, workplaces and laboratories. The Company is working to develop delta-9 tetrahydrocannabinol (THC) and alcohol screening devices. Delta-9 THC is the psychoactive component of marijuana. Breath testing for delta-9 THC would allow employers and law enforcement to identify recent marijuana use. It develops contactless breath alcohol detection devices. Its technologies include THC Breathalyzer System, Breath Logix Alcohol Breathalyzer and Breath Logix Vehicle. THC Breathalyzer System includes Cannabix Mass Spectrum (MS) Breath Sampler (MSBS) and Cannabix Breath Collection Unit (BCU). The Breath Logix (BR-LGX) Industrial Series is a fully automated breath alcohol screening system for employee, contractor and visitor access control. The BR-LGX vehicle device could be integrated into various locations in the vehicle cabin. It also develops Cannabix FAIMS device.


CSE:BLO - Post by User

Post by davgroon May 04, 2021 9:02am
206 Views
Post# 33122138

Receives Notice of Allowance for Detection of Molecules

Receives Notice of Allowance for Detection of Molecules
Cannabix Technologies Receives Notice of Allowance for Detection of Molecules - FAIMS U.S. Patent


2021-05-04 08:45 ET - News Release
 

VANCOUVER, British Columbia, May 04, 2021 (GLOBE NEWSWIRE) -- Cannabix Technologies Inc. (CSE: BLO) (OTC PINK: BLOZF) (the “Company” or “Cannabix”) developer of the Cannabix Marijuana Breathalyzer devices for law enforcement and the workplace, reports that it has received a notice of allowance from the United States Patent and Trademark Office (USPTO) for patent application, No. 17/019728 entitled, “Apparatus and Methods for Detection of Molecules”. This Track 1 patent application is centered on innovations made by Cannabix with its FAIMS (field asymmetric waveform ion mobility spectrometry) based marijuana breathalyzer technology. This patent application is the culmination of research and development work conducted by Cannabix scientists and engineers in the areas of ion mobility spectrometry, non-volatile molecule sampling and fluid dynamics. These developments will provide utility in several areas related to detection of molecules in breath.

Cannabix is using its FAIMS technology to isolate and detect 9-tetrahydrocannabinol (“THC”), a non-volatile compound, in breath. The Cannabix FAIMS marijuana breathalyzer device uses ion mobility filtering techniques (related to mass spectrometry – the gold standard analytical technique for molecular detection). The Cannabix device has been designed and built in a series of modules that together allow for sample intake, ionization, filtering and detection – all done under atmospheric pressure. In addition, the device has the ability to couple directly to a mass spectrometer in order to validate its detector responses.

It should be noted that a notice of allowance from the USPTO does not constitute a grant of patent. The Company will report on future material developments regarding its “Apparatus and Methods for Detection of Molecules” patent application in due course.

The Company has been steadily growing its intellectual property (IP) portfolio over recent months. In April, the Company received a notice of allowance for its patent application, No. 2887841 entitled, “Cannabis Drug Detection Device” from the Canadian Intellectual Property Office (CIPO). In addition, the Company was granted a granted patent No. 14/689434 entitled, “Cannabis Drug Detection Device” from the USPTO in January 2021. The newly received notice of allowance from the USPTO for its FAIMS related patent application builds on this IP momentum.

The Company expects to ship its THCBA to a clinic in the Northwestern, U.S. for beta testing in May. The collaborating clinic has a robust drug testing operation and is one of the top drug testing providers to employers within its respective state. The beta testing is focussed on improving user experience through testing and feedback and training the device’s machine learning database.

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