DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
Claim 1. A method for run-time cavity ring-down spectroscopy pattern recognition, the method comprising:
generating, with a sensor, a set of estimated ring-down data for an analyte;
generating a spectrum of the analyte based on the set of estimated ring-down data for the analyte;
generating a sample-invariant molecular fingerprint of the analyte based on the spectrum of the analyte;
determining one or more metrics between the sample-invariant molecular fingerprint of the analyte and predetermined molecular fingerprints of a plurality of reference samples; and
identifying a match between the analyte and at least one of the plurality of reference samples based on the one or more metrics.
Claim 8. A system for run-time cavity ring-down spectroscopy pattern recognition, the system comprising:
a sensor configured to generate a set of estimated ring-down data for an analyte; and
an analyte identifier configured to:
generate a spectrum of the analyte based on the set of estimated ring-down data for the analyte,
generate a sample-invariant molecular fingerprint of the analyte based on the spectrum of the analyte,
determine one or more metrics between the sample-invariant molecular fingerprint of the analyte and predetermined molecular fingerprints of a plurality of reference samples, and
identify a match between the analyte and at least one of the plurality of reference samples based on the one or more metrics.
Claim 15. One or more tangible, non-transitory computer-readable mediums storing instructions that, when executed, cause one or more processing devices to:
generate a set of estimated ring-down data for an analyte;
generate a spectrum of the analyte based on the set of estimated ring-down data for the analyte;
generate a sample-invariant molecular fingerprint of the analyte based on the spectrum of the analyte;
determine one or more metrics between the sample-invariant molecular fingerprint of the analyte and predetermined molecular fingerprints of a plurality of reference samples; and
identify a match between the analyte and at least one of the plurality of reference samples based on the one or more metrics.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly, there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a sensor are generic computer elements and not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a run-time cavity ring-down spectroscopy and an analyte identifier is considered field of use or technological environment in which when applied to the judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Claim 6, 13, and 20 recite emitting a pulse train into an optical cavity including the analyte, receiving a response pulse train from the optical cavity. These claims recite what is considered necessary data gathering and is not sufficient to integrate the abstract idea into a practical application.
Claim 13 recites, an optical cavity, a light emitter and a light detector. These claims recite what is considered field of use or technological environment in which when applied to the judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Claims 2-5, 7, 9-12, 14, and 16-19 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 6, 8, 13, 15, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cormier et al. (US 2010/0002234 A1) hereinafter Cormier.
Regarding Claim 1, Cormier teaches generating, with a sensor, a set of estimated ring-down data for an analyte ([0040] “a series of mode-matched and frequency-matched light pulses of known distinct frequencies into the ringdown cavity formed by the measurement chamber mirrors, measure the light pulse decay time within the ringdown cavity by using a detector (i.e., sensor) which is responsive to the intensity of light within the measurement chamber, sample and store the signals produced by the detector,”); generating a spectrum of the analyte based on the set of estimated ring-down data for the analyte ([0018] “ The present invention also provides for a method of analysis of the electromagnetic absorption spectrum of a complex sample u comprising the assessment of absorption at N discrete frequencies, comprising” where [0025] “G) Steps D through F are repeated until no further improvements to the goodness-of-fit of the model to the measurement vector y can be achieved by adding or removing analytes to the model.”); generating a sample-invariant molecular fingerprint of the analyte based on the spectrum of the analyte ([0066] “This type of laser is particularly useful since the 920 cm−1 to 1020 cm−1 frequency range where principal-isotope CO2 lasers emit over 50 lines lies within the previously-defined highly-transparent interval of the “fingerprint” region of the infrared spectrum. Single-mode, low-pressure CO2 lasers are highly monochromatic, a feature that facilitates coupling the fundamental optical resonance mode of the ringdown cavity.” Where [0037] “. In particular, molecules composed of two or more atoms show highly distinct absorption bands, characteristic of their vibrational and rotational energy levels, in the molecular “fingerprint” frequency region of the infrared spectra between 100 cm−1 and 2000 cm−1. Thus, hundreds of substances, for example volatile organic compounds (VOCs) which are relevant to medical monitoring and disease diagnosis using exhaled breath or other exhalations”); determining one or more metrics between the sample-invariant molecular fingerprint of the analyte and predetermined molecular fingerprints of a plurality of reference samples ([0082] “Extensive libraries of absorption cross-sections, measured for various temperatures at ambient atmospheric pressure (i.e., metrics), have been compiled for hundreds of compounds of interest, e.g. the PPNL and NIST databases (Gas Phase Databases for Quantitative Infrared Spectroscopy, Sharpe et. al., Applied Spectroscopy Vol 58, No 12, 2004). More detailed pressure and temperature correction parameters have also been measured by several research groups for various molecules, and this data is available in the scientific literature.” Where [0083] “The use of absorption cross-sections to model the absorption by a molecule at a given frequency is a simpler and faster approach than the line-by-line modeling methods often used for detailed analysis of gas-phase infrared spectra. At a given temperature and pressure, the absorption cross-section for each absorbing analyte at each laser frequency provides sufficient information to obtain accurate quantification (i.e., metrics) of the analytes of interest. In contrast, if the usual line-by-line technique were used, accurate knowledge of line centers, line strengths and temperature and pressure broadening parameters would be required for all the absorbing analytes in the mixture in order to compute the optical density at the frequency, pressure and temperature of interest. Since line-by-line absorption parameters have only been defined for a handful of small molecules, the use of absorption cross-sections becomes necessary for the analysis of complex mixtures.”); and identifying a match between the analyte and at least one of the plurality of reference samples based on the one or more metrics ([0089] “The goodness-of-fit may be estimated by calculating a fit parameter that incorporates the degrees of freedom (entropy) in the model, which in this case is related to the total number of analytes in the nj vector. An entropy-based goodness-of-fit metric such as the Akaike Information Criterion (ATC) can be used for this purpose (Akaike, Hirotsugu (1974) “A new look at the statistical model identification”. IEEE Truansactions on Automatic Control 19 (6): 716-723)). When using an entropy-based fit criterion it is possible that removing an analyte can improve the fit of the model to the measurement, although typically an added analyte improves the fit.”).
Regarding Claim 8, Cormier teaches a sensor configured to generate a set of estimated ring-down data for an analyte ([0040] “a series of mode-matched and frequency-matched light pulses of known distinct frequencies into the ringdown cavity formed by the measurement chamber mirrors, measure the light pulse decay time within the ringdown cavity by using a detector (i.e., sensor) which is responsive to the intensity of light within the measurement chamber, sample and store the signals produced by the detector,”); and an analyte identifier ([0040] “The apparatus (i.e., analyte identifier) of the present invention is intended to collect a gas or liquid sample containing at least one analyte to be identified and quantified,”) configured to: generate a spectrum of the analyte based on the set of estimated ring-down data for the analyte ([0018] “ The present invention also provides for a method of analysis of the electromagnetic absorption spectrum of a complex sample u comprising the assessment of absorption at N discrete frequencies, comprising” where [0025] “G) Steps D through F are repeated until no further improvements to the goodness-of-fit of the model to the measurement vector y can be achieved by adding or removing analytes to the model.”); generate a sample-invariant molecular fingerprint of the analyte based on the spectrum of the analyte ([0066] “This type of laser is particularly useful since the 920 cm−1 to 1020 cm−1 frequency range where principal-isotope CO2 lasers emit over 50 lines lies within the previously-defined highly-transparent interval of the “fingerprint” region of the infrared spectrum. Single-mode, low-pressure CO2 lasers are highly monochromatic, a feature that facilitates coupling the fundamental optical resonance mode of the ringdown cavity.” Where [0037] “. In particular, molecules composed of two or more atoms show highly distinct absorption bands, characteristic of their vibrational and rotational energy levels, in the molecular “fingerprint” frequency region of the infrared spectra between 100 cm−1 and 2000 cm−1. Thus, hundreds of substances, for example volatile organic compounds (VOCs) which are relevant to medical monitoring and disease diagnosis using exhaled breath or other exhalations”); determine one or more metrics between the sample-invariant molecular fingerprint of the analyte and predetermined molecular fingerprints of a plurality of reference samples ([0082] “Extensive libraries of absorption cross-sections, measured for various temperatures at ambient atmospheric pressure (i.e., metrics), have been compiled for hundreds of compounds of interest, e.g. the PPNL and NIST databases (Gas Phase Databases for Quantitative Infrared Spectroscopy, Sharpe et. al., Applied Spectroscopy Vol 58, No 12, 2004). More detailed pressure and temperature correction parameters have also been measured by several research groups for various molecules, and this data is available in the scientific literature.” Where [0083] “The use of absorption cross-sections to model the absorption by a molecule at a given frequency is a simpler and faster approach than the line-by-line modeling methods often used for detailed analysis of gas-phase infrared spectra. At a given temperature and pressure, the absorption cross-section for each absorbing analyte at each laser frequency provides sufficient information to obtain accurate quantification (i.e., metrics) of the analytes of interest. In contrast, if the usual line-by-line technique were used, accurate knowledge of line centers, line strengths and temperature and pressure broadening parameters would be required for all the absorbing analytes in the mixture in order to compute the optical density at the frequency, pressure and temperature of interest. Since line-by-line absorption parameters have only been defined for a handful of small molecules, the use of absorption cross-sections becomes necessary for the analysis of complex mixtures.”); and identify a match between the analyte and at least one of the plurality of reference samples based on the one or more metrics ([0089] “The goodness-of-fit may be estimated by calculating a fit parameter that incorporates the degrees of freedom (entropy) in the model, which in this case is related to the total number of analytes in the nj vector. An entropy-based goodness-of-fit metric such as the Akaike Information Criterion (ATC) can be used for this purpose (Akaike, Hirotsugu (1974) “A new look at the statistical model identification”. IEEE Truansactions on Automatic Control 19 (6): 716-723)). When using an entropy-based fit criterion it is possible that removing an analyte can improve the fit of the model to the measurement, although typically an added analyte improves the fit.”).
Regarding Claim 15, Cormier teaches generate a set of estimated ring-down data for an analyte ([0040] “a series of mode-matched and frequency-matched light pulses of known distinct frequencies into the ringdown cavity formed by the measurement chamber mirrors, measure the light pulse decay time within the ringdown cavity by using a detector (i.e., sensor) which is responsive to the intensity of light within the measurement chamber, sample and store the signals produced by the detector,”); generate a spectrum of the analyte based on the set of estimated ring-down data for the analyte ([0018] “ The present invention also provides for a method of analysis of the electromagnetic absorption spectrum of a complex sample u comprising the assessment of absorption at N discrete frequencies, comprising” where [0025] “G) Steps D through F are repeated until no further improvements to the goodness-of-fit of the model to the measurement vector y can be achieved by adding or removing analytes to the model.”);
generate a sample-invariant molecular fingerprint of the analyte based on the spectrum of the analyte ([0066] “This type of laser is particularly useful since the 920 cm−1 to 1020 cm−1 frequency range where principal-isotope CO2 lasers emit over 50 lines lies within the previously-defined highly-transparent interval of the “fingerprint” region of the infrared spectrum. Single-mode, low-pressure CO2 lasers are highly monochromatic, a feature that facilitates coupling the fundamental optical resonance mode of the ringdown cavity.” Where [0037] “. In particular, molecules composed of two or more atoms show highly distinct absorption bands, characteristic of their vibrational and rotational energy levels, in the molecular “fingerprint” frequency region of the infrared spectra between 100 cm−1 and 2000 cm−1. Thus, hundreds of substances, for example volatile organic compounds (VOCs) which are relevant to medical monitoring and disease diagnosis using exhaled breath or other exhalations”); determine one or more metrics between the sample-invariant molecular fingerprint of the analyte and predetermined molecular fingerprints of a plurality of reference samples ([0082] “Extensive libraries of absorption cross-sections, measured for various temperatures at ambient atmospheric pressure (i.e., metrics), have been compiled for hundreds of compounds of interest, e.g. the PPNL and NIST databases (Gas Phase Databases for Quantitative Infrared Spectroscopy, Sharpe et. al., Applied Spectroscopy Vol 58, No 12, 2004). More detailed pressure and temperature correction parameters have also been measured by several research groups for various molecules, and this data is available in the scientific literature.” Where [0083] “The use of absorption cross-sections to model the absorption by a molecule at a given frequency is a simpler and faster approach than the line-by-line modeling methods often used for detailed analysis of gas-phase infrared spectra. At a given temperature and pressure, the absorption cross-section for each absorbing analyte at each laser frequency provides sufficient information to obtain accurate quantification (i.e., metrics) of the analytes of interest. In contrast, if the usual line-by-line technique were used, accurate knowledge of line centers, line strengths and temperature and pressure broadening parameters would be required for all the absorbing analytes in the mixture in order to compute the optical density at the frequency, pressure and temperature of interest. Since line-by-line absorption parameters have only been defined for a handful of small molecules, the use of absorption cross-sections becomes necessary for the analysis of complex mixtures.”); and identify a match between the analyte and at least one of the plurality of reference samples based on the one or more metrics ([0089] “The goodness-of-fit may be estimated by calculating a fit parameter that incorporates the degrees of freedom (entropy) in the model, which in this case is related to the total number of analytes in the nj vector. An entropy-based goodness-of-fit metric such as the Akaike Information Criterion (ATC) can be used for this purpose (Akaike, Hirotsugu (1974) “A new look at the statistical model identification”. IEEE Truansactions on Automatic Control 19 (6): 716-723)). When using an entropy-based fit criterion it is possible that removing an analyte can improve the fit of the model to the measurement, although typically an added analyte improves the fit.”).
Regarding Claim 6 and 20, Cormier teaches the limitations of claim 1 and 15, respectively.
Cormier further teaches emitting a pulse train into an optical cavity including the analyte ([0040] “The apparatus of the present invention is intended to collect a gas or liquid sample containing at least one analyte to be identified and quantified, wherein a portion of the sample is directed into a measurement chamber (i.e., optical cavity) which contains highly-reflective mirrors, measure (and optionally regulate) the pressure and temperature of the sample in the measurement chamber, shine a series of mode-matched and frequency-matched light pulses of known distinct frequencies into the ringdown cavity formed by the measurement chamber mirrors,”, and [0012] an apparatus comprising a source capable of emitting pulsed light”); receiving a response pulse train from the optical cavity ([0040] “measure the light pulse decay time within the ringdown cavity by using a detector which is responsive to the intensity of light within the measurement chamber, sample and store the signals produced by the detector, calculate the light attenuation due to the sample mixture in the measurement chamber, identify the analytes present in the sample mixture and calculate their concentrations.”), and generating the set of estimated ring-down data for the analyte based on the response pulse train ([0040] “measure the light pulse decay time within the ringdown cavity by using a detector which is responsive to the intensity of light within the measurement chamber, sample and store the signals produced by the detector, calculate the light attenuation due to the sample mixture in the measurement chamber, identify the analytes present in the sample mixture and calculate their concentrations.”).
Regarding Claim 13, Cormier teaches the limitations of claim 8.
Cormier further teaches an optical cavity including the analyte ([0040] “The apparatus of the present invention is intended to collect a gas or liquid sample containing at least one analyte to be identified and quantified, wherein a portion of the sample is directed into a measurement chamber (i.e., optical cavity) which contains highly-reflective mirrors, measure (and optionally regulate) the pressure and temperature of the sample in the measurement chamber, shine a series of mode-matched and frequency-matched light pulses of known distinct frequencies into the ringdown cavity formed by the measurement chamber mirrors,”), a light emitter configured to emit a pulse train into the optical cavity ([0040] “The apparatus of the present invention is intended to collect a gas or liquid sample containing at least one analyte to be identified and quantified, wherein a portion of the sample is directed into a measurement chamber (i.e., optical cavity) which contains highly-reflective mirrors, measure (and optionally regulate) the pressure and temperature of the sample in the measurement chamber, shine a series of mode-matched and frequency-matched light pulses of known distinct frequencies into the ringdown cavity formed by the measurement chamber mirrors,”, and [0012] an apparatus comprising a source capable of emitting pulsed light”) and a light detector configured to receive to a response pulse train from the optical cavity ([0040] “measure the light pulse decay time within the ringdown cavity by using a detector which is responsive to the intensity of light within the measurement chamber, sample and store the signals produced by the detector, calculate the light attenuation due to the sample mixture in the measurement chamber, identify the analytes present in the sample mixture and calculate their concentrations.”), wherein, to generate the set of estimated ring-down data for the analyte, the sensor is further configured to generate the set of estimated ring-down data for the analyte based on the response pulse train ([0040] “measure the light pulse decay time within the ringdown cavity by using a detector which is responsive to the intensity of light within the measurement chamber, sample and store the signals produced by the detector, calculate the light attenuation due to the sample mixture in the measurement chamber, identify the analytes present in the sample mixture and calculate their concentrations.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-4, 9-11, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cormier in view of Rajapaksha, et al. (A Rapid and Sensitive Chemical Screening Method for E-Cigarette Aerosols Based on Runtime Cavity Ringdown Spectroscopy, 2021, Environ. Sci. Technol. 2021, 55, 8090−8096) hereinafter Rajapaksha.
Regarding Claim 2, 9, and 16, Cormier teaches the limitations of claims 1, 8, and 15, respectively.
Cormier does not teach wherein generating the sample-invariant molecular fingerprint of the analyte includes subtracting the spectrum of the analyte from a spectrum of a background gas.
Rajapaksha teaches wherein generating the sample-invariant molecular fingerprint of the analyte includes subtracting the spectrum of the analyte from a spectrum of a background gas (pg. 8092 col 1 pp 3 “Because measurements are taken at atmospheric pressure, the spectrum of the sample includes effects of the background atmosphere. To compensate, the AG-4000 then divides the spectrum of the sample with a previously measured background spectrum to create a unique molecular fingerprint for the unknown sample”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the background subtraction discussed in Rajapaksha to the ring down spectroscopy taught in Cormier for the purpose of having a clean signal when tests are performed in the atmosphere. This is advantageous because it helps to remove noise that is not related to the analyte.
Regarding Claim 3, 10, and 17, Cormier teaches the limitations of 1, 8 and 15, respectively.
Cormier does not teach filtering the sample-invariant molecular fingerprint using a low-order median filter or a multi-scan averaging filter.
Rajapaksha teaches filtering the sample-invariant molecular fingerprint using a low-order median filter or a multi-scan averaging filter (pg. 8092 col 1 pp 2 “Five sets of spectral data were captured for each sample and the average was used to plot the IR spectrum of each sample (i.e., a multi-scan averaging filter)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the background subtraction discussed in Rajapaksha to the ring down spectroscopy taught in Cormier for the purpose of having a clean signal to analyze after the tests are run. This is advantageous because it helps to remove noise that is not related to the analyte.
Regarding Claim 4, 11, and 18, Cormier teaches the limitations of claims 1, 8, and 15, respectively.
Cormier does not teach wherein the one or more metrics including a Pearson correlation coefficient .
Rajapaksha teaches wherein the one or more metrics including a Pearson correlation coefficient (pg. 8092 col 2 pp 1 “To identify an unknown aerosol’s molecular fingerprint, the AG-4000 calculates the Pearson correlation coefficient between the sample’s fingerprint vector and that of the library’s entries to find the best match”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the background subtraction discussed in Rajapaksha to the ring down spectroscopy taught in Cormier for the purpose of best analyzing the signal to identify the analyte. This is advantageous because it mathematically correlates the best matching reference to the analyte.
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cormier in view of Gulati et al. (WO 2016/054079 A1) hereinafter Gulati.
Regarding Claim 5, 12, and 19, Cormier teaches the limitations of claims 1, 8, and 15, respectively.
Cormier teaches Ring down spectroscopy ([0003] “Cavity ringdown spectroscopy (CRDS)”)
Cormier does not teach dividing the set of data for the analyte into a plurality of intervals; determining ratios representing relative signal power during each of the plurality of intervals; and determining the spectrum of the analyte by comparing the ratios and corresponding wavelengths.
Gulati teaches dividing the set of data for the analyte into a plurality of intervals ([0371] “The observed spectrum O is divided into several (e.g., 2, 4, 5, 10, 15, 18, 24, 32, 50, etc.) regions (i.e., plurality of intervals) called features.”; [0598] “The feature generator 6112 also includes a feature extractor 6116, which divides the spectrum generated by the spectrum generator 6114 into one or more regions determined according to wavelength or wavenumber ranges. The individual portions of the spectrum corresponding to these region(s) are called features.”), determining ratios representing relative signal power during each of the plurality of intervals ([0299] “The selection of a soliton family to construct a Zyoton (i.e., noted in [0020] as a mathematical representation of the sensor data), and its time-domain and frequency-domain parameterization, is based on the consideration of at least one of several specific attributes, which include: the signal-to-noise ratio (SNR) of the measurement system, the anticipated signal-to-clutter ratio (SCR) or signal-to-clutter noise ratio (SCNR), the degree of desired SCR increase, the desired analyte quantitation accuracy and precision, resolution, specificity, dynamic range, sensitivity, and concentration range over which linearity is desired. The SCNR (signal to clutter noise power ratio or signal to coherent noise power ratio) is generally defined as the ratio of signal power to the power of the sum of clutter plus background non-coherent noise power. Clutter can be due to signal from confounders or from coherent noise” where this is different for different interval ranges based on detector strength across wavelengths, filters power attached across wavelengths, power of the lasers and the backgrounds materials reactivity to the laser light when looking over wavelength ranges, i.e., laser and their harmonics would have a signal in a specific region or interval, and not in another. ), and determining the spectrum of the analyte by comparing the ratios and corresponding wavelengths ([0599] “As described above, each feature represents the energy absorbed by the analyte and/or one or more confounders, typically in the presence of noise and along with scattering losses, in the wavelength region associated with the feature. Therefore, the conditioned features derived from one or more features and one or more carrier kernels also generally represent the energy absorbed by the analyte and/or one or more confounders, along with noise and scattering losses, in corresponding wavelength regions.” Where [0602] “Based on the measured quantity, the quantitation module 6148 can determine whether the analyte is present or absent in the medium to be analyzed 618. For example, the analyte may be determined to be present if the analyte quantity or concentration is greater than a specified threshold. Further details of the process of projection are described below.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the use of intervals to detect the analyte as discussed in Gulati to the rind down spectroscopy discussed in Cormier for the purpose of detecting the analyte content of specific regions. This is advantageous because it allows for the highlighting and determination of specific compounds within the analyte.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cormier in view of Sorensen et al. (US 20220026441 A1) hereinafter Sorensen.
Regarding Claim 7 and 14, Cormier teaches the limitations of claims 1 and 8.
Cormier does not teach wherein the match between the analyte and the at least one of the plurality of reference samples is identifying using one or more machine learning models.
Sorensen teaches wherein the match between the analyte and the at least one of the plurality of reference samples is identifying using one or more machine learning models ([0191] “For example without limiting the disclosure, in at least one embodiment at least one spectrum is classified (i.e., samples matched to a reference) using a machine learning method such as support vector machines. One skilled in the art will appreciate that certain machine learning methods are typically performed using machine learning models that have previously been trained on training data with properties related to the data to be classified.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the use of machine learning tools as discussed in Sorensen to the ring down spectroscopy discussed in Cormier for the purpose of automating the detection of the analytes. This is advantageous because the machine learning can adjust their determination based on training sets and speed up the process of analyte detection.
Conclusion
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/EMMA ALEXANDER/Patent Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863