公开(公告)号：US20210190747A1

公开(公告)日：2021-06-24

申请号：US16721035

申请日：2019-12-19

Applicant: X Development LLC

Inventor： Bennet Joseph Cobley ,  Rohinton Mehta ,  Radu Gogoana

IPC: G01N33/00

Abstract: Methods, systems, and apparatus for a camera-enhanced multi-modal gas sensing apparatus including a camera configured to capture imaging data including at least a portion of a test environment including the gas sensing apparatus and an object of interest within the field of view of the camera, multiple gas sensors including a first type of gas sensor and a second type of gas sensor different from the first type of gas sensor which are each sensitive to a respective set of analytes, a housing configured to hold the multiple gas sensors, a gas inlet coupled to the housing and configured to expose the multiple gas sensors to a gas introduced from the test environment via the gas inlet, and a data processing apparatus in data communication with the multiple gas sensors and the camera.

公开(公告)号：US20210190749A1

公开(公告)日：2021-06-24

申请号：US17028245

申请日：2020-09-22

Applicant: X Development LLC

Inventor： Bennet Joseph Cobley ,  Rohinton Mehta

IPC: G01N33/00 ,  G01N27/12

Abstract: Methods, systems, and apparatus for a multi-modal gas sensing system including a set of multi-modal gas sensor modules, where each module includes multiple gas sensors that are sensitive to a respective set of analytes. The set of modules are provided to a receptacle configured to receive modules including a gas manifold in fluidic contact with each of the modules, and a data processing apparatus in data communication with each module received by the receptacle. A sampling sequence is configured for the set of modules based in part on a set of analytes of interest representative of a test environment. A test gas is provided in turn to a first proper subset and a second proper subset of the modules and a first set and a second set of response data are collected, where the first and second sets of response data are provided as training data to a machine-learned model.