Viavi Solutions Inc.

Christopher G Pederson

Changmeng Hsiung

Marc K Von Gunten

Nada A O'Brien

Lan Sun

Curtis Ross Hruska

Charles Andrew Hulse

Brett Joseph Bryars

Jerry Zieba

Peng Zou

Benjamin F Catching

Brett J Bryars

A device may receive, from a plurality of sensors, sensor data relating to a user. The device may include a plurality of types of sensors including a spectrometer and one or more of an accelerometer, a heart rate sensor, a blood pressure sensor, a blood sugar sensor, a perspiration sensor, a skin conductivity sensor, or an imaging sensor. The device may process the sensor data, from the plurality of types of sensors, relating to the user to determine a health condition of the user. The device may provide, via a user interface, information identifying the health condition of the user based on processing the sensor data, from the plurality of types of sensors, relating to the user.

Health tracking device

A device may receive information identifying results of a set of spectroscopic measurements of a training set of known samples and a validation set of known samples. The device may generate a classification model based on the information identifying the results of the set of spectroscopic measurements, wherein the classification model includes at least one class relating to a material of interest for a spectroscopic determination, and wherein the classification model includes a no-match class relating to at least one of at least one material that is not of interest or a baseline spectroscopic measurement. The device may receive information identifying a particular result of a particular spectroscopic measurement of an unknown sample. The device may determine whether the unknown sample is included in the no-match class using the classification model. The device may provide output indicating whether the unknown sample is included in the no-match class.

Reduced false positive identification for spectroscopic classification

A device may receive information identifying results of a spectroscopic measurement of an unknown sample. The device may perform a first classification of the unknown sample based on the results of the spectroscopic measurement and a global classification model. The device may generate a local classification model based on the first classification. The device may perform a second classification of the unknown sample based on the results of the spectroscopic measurement and the local classification model. The device may provide information identifying a class associated with the unknown sample based on performing the second classification.

Identification using spectroscopy

A device may obtain a master calibration set, associated with a master calibration model of a master instrument, that includes spectra, associated with a set of samples, generated by the master instrument. The device may identify a selected set of master calibrants based on the master calibration set. The device may obtain a selected set of target calibrants that includes spectra, associated with the subset of the set of samples, generated by the target instrument. The device may create a transfer set based on the selected set of master calibrants and the selected set of target calibrants. The device may create a target calibration set, corresponding to the master calibration set, based on the transfer set. The device may generate, using an optimization technique associated with the transfer set and a support vector regression modeling technique, a transferred calibration model, for the target instrument, based on the target calibration set.

Transfer of a calibration model using a sparse transfer set

A medication assurance system for verification of both the medication and the patient is disclosed. A portable spectrometer is used to obtain a light spectrum of the medication. A subject identification or biometric device is used to identify the patient. A controller coupled to the portable spectrometer and the subject identification device identifies the medication by performing a chemometric analysis of the light spectrum. Based on the medication identified and the patient identified, the controller can determine if the medication is to be taken by the patient.

Medication assurance system and method

A portable spectrometer device includes an illumination source for directing at a sample, and a tapered light pipe (TLP) for capturing light interacting with the sample at a first focal ratio and for delivering the light at a second focal ratio lower than the first focal ratio. A linearly variable filter (LVF) separates the captured light into a spectrum of constituent wavelength signals; and a detector array, including a plurality of pixels, each of the plurality of pixels disposed to receive at least a portion of a plurality of the constituent wavelength signals provides a power reading for each constituent wavelength. Preferably, the TLP is lensed at one end, and recessed in a protective boot with stepped inner walls. The gap between the TLP and LVF is minimized to further enhance resolution and robustness.

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Santa Rosa, CA, United States of America

Christopher G Pederson