Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The extensive documents list (100+) on the IDSs filed 12/26/2023 and 07/24/2024 have been given only a cursory review to the extent that routine search returning 100+ documents would receive. If any of the references cited by the Applicant are of particular relevance than the Applicant should directly address the document and sections.
Allowable Subject Matter
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 18 recites the limitation "the buffer" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-6, 11-17, and 19 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Angulo et al. (US Pub # 2023/0197207, e.f.d. 12/16/21) in view of Chen et al (US Pub # 2023/0393060).
Regarding claims 1 and 13, Angulo discloses a system and method for determining predicted component concentrations of a target mixture (abstract; [0008]), comprising:
providing a mid-infrared sensor configured to measure absorption spectra (FTIR spectrometer; FIG 1); and
providing a computer comprising a processor and a data storage storing computer program instructions (implicit the ML algorithm for mixture composition determination; FIG 1; [0013]) operable to cause the processor to:
receive, from the mid-infrared sensor, first training absorption spectra for a plurality of training mixtures, the plurality of training mixtures each having one or more components associated with the target mixture and comprises different predetermined component concentrations (FTIR spectrometer acquires plurality of training mixtures having components associated with target mixtures with different known concentrations; FIG 1-2; [0005-0007]);
train a first machine learning model using the first training absorption spectra to obtain a first trained machine learning model, the first trained machine learning model being adapted to classify an absorption spectrum of a mixture having one or more of the components associated with the target mixture to identify specific component concentrations of the mixture, the identified specific component concentrations being one of the different predetermined component concentrations (trained model is used to extract a plurality of features via principal component analysis; [0006], [0008]);
receive, from the mid-infrared sensor, a target absorption spectrum of the target mixture (multicomponent mixture is scanned using FTIR spectroscopy; [0008]); and
determine the predicted component concentrations of the target mixture by classifying the target absorption spectrum using the first trained machine learning model (the machine learning model obtains a concentration of one or more constituent components; [0008]).
Angulo does not specify a MWIR waveguide sensor. In the same field of endeavor, Chen discloses a MWIR waveguide gas sensor configured for determining concentrations of gases in a sample ([0004, 0017, 0018]), with the benefit of wide application, high sensitivity, and efficiency ([0006]). In light of the teachings of CHEN, it would have been obvious to one of ordinary skill in the art at the time of the invention to combine with the teachings of Angulo.
Regarding claims 2 and 14, Angulo discloses training and implementing a plurality of different types of machine learning models (SVR, KNN, DT, RF, LR and ANN; [0009]), wherein it is implied or at least makes obvious the additional steps necessary for training and applying a second machine learning model, including:
receiving, from the mid-infrared waveguide sensor, second training absorption spectra for the plurality of training mixtures;
training a second machine learning model using the second training absorption spectra to obtain a second trained machine learning model, the second trained machine learning model being adapted to decompose the absorption spectrum of the mixture into component absorption spectra associated with components of the mixture; and
decomposing the target absorption spectrum into target component absorption spectra using the second trained machine learning model, each of the target component absorption spectra being associated with a corresponding component of the target mixture and comprises a predetermined number of data points across measured wavelengths of the target absorption spectrum ([0006-0013]).
Regarding claims 3 and 15, Angulo discloses the instruction cause the processor to: receive, from the mid-infrared sensor, measured absorption spectra of each of the components of the target mixture, the measured absorption spectra of each of the components include a series of measured absorption spectra of varying concentrations of a respective component; apply linear fitting to the measured absorption spectra of each of the components to determine predetermined wavelengths of the measured absorption spectra for comparison; and compare each of the target component absorption spectra of the target mixture with the measured absorption spectra of a corresponding component at the predetermined wavelengths to determine a predicted component concentration for each of the components of the target mixture, wherein the predicted component concentration is associated with a concentration of the corresponding component having the measured absorption spectrum that best fits the target component absorption spectrum of the corresponding component of the target mixture at the predetermined wavelengths (Linear Regression model; [0008-0009]).
Regarding claims 4 and 16, Angulo discloses wherein the predicted component concentration for each of the components of the target mixture includes a plurality of predicted component concentrations, each of the plurality of the predicted component concentrations being associated with a corresponding one of the predetermined wavelengths wherein the data storage of the computer further stores computer program instructions operable to cause the processor to: average the plurality of predicted component concentrations to obtain an average predicted component concentration for each of the components of the target mixture (determining mean absolute error (MAE) and R2; FIG 11; [0099]).
Regarding claims 5 and 17, Angulo discloses a MLP regressor model (ANN or multi-layer perceptron regressor; TABLES 4, 6, 7; [0005, 0007]).
Regarding claims 6 and 19, Angulo discloses wherein the first machine learning model includes a convolutional neural network (CNN)([0032]; FIG. 18).
Regarding claim 11, Angulo discloses a system for determining predicted component concentrations of a target mixture ([0008-0013]), the system comprising:
a mid-infrared sensor configured to measure absorption spectra (FTIR spectrometer; [0012]); and
a computer comprising a processor and a data storage storing computer program instructions ([0011-0013]) operable to cause the processor to:
receive, from the mid-infrared waveguide sensor, training absorption spectra for a plurality of training mixtures, the plurality of training mixtures each having one or more components associated with the target mixture and comprises different predetermined component concentrations (obtaining a spectrum for each mixture of a plurality of mixtures of the constituent components, where concentration of each constituent component is known for each mixture of the plurality of mixtures, and a plurality of features are extracted from each of the obtained spectra; [0006]);
train a machine learning model using the training absorption spectra to obtain a trained machine learning model, the trained machine learning model being adapted to decompose an absorption spectrum of a mixture into component absorption spectra associated with components of the mixture, wherein the components of the mixture include one or more components of the target mixture (machine learning model is trained using the extracted features; [0006]);
receive, from the mid-infrared waveguide sensor, a target absorption spectrum of the target mixture (obtaining a spectrum of the multicomponent mixture produced by scanning the mixture using FTIR spectroscopy; [0008]);
decompose the target absorption spectrum into target component absorption spectra using the trained machine learning model, each of the target component absorption spectra being associated with a corresponding component of the target mixture and comprises a predetermined number of data points across measured wavelengths of the target absorption spectrum (a plurality of features is extracted from each of the obtained spectra using PCA, and obtains a concentration of one or more constituent components of the multicomponent mixture from the trained machine learning model; [0008-0009]);
receive, from the mid-infrared sensor, measured absorption spectra of each of the components of the target mixture, the measured absorption spectra include a series of measured absorption spectra of varying concentrations of a respective component (FIG 2; [0016, 0085]);
apply linear fitting to the measured absorption spectra of each of the components of the target mixture to determine predetermined wavelengths of the measured absorption spectra for comparison (linear regression (LR) + PCA fitting outperformed aNN in R2; [0009, 0037]); and
compare each of the target component absorption spectra of the target mixture with the measured absorption spectra of a corresponding component at the predetermined wavelengths to determine a predicted component concentration for each of the components of the target mixture, wherein the predicted component concentration is associated with a concentration of the corresponding component having the measured absorption spectrum that best fits the target component absorption spectrum of the corresponding component of the target mixture at the predetermined wavelengths (LR + PCA produced with an R2 implies comparison with predetermined wavelengths; [0009, 0037]).
Angulo does not specify a MWIR waveguide sensor. In the same field of endeavor, Chen discloses a MWIR waveguide gas sensor configured for determining concentrations of gases in a sample ([0004, 0017, 0018]), with the benefit of wide application, high sensitivity, and efficiency ([0006]). In light of the teachings of CHEN, it would have been obvious to one of ordinary skill in the art at the time of the invention to combine with the teachings of Angulo.
Regarding claim 12, Angulo discloses wherein the predicted component concentration for each of the components of the target mixture includes a plurality of predicted component concentrations, each of the plurality of the predicted component concentrations being associated with a corresponding one of the predetermined wavelengths wherein the data storage of the computer further stores computer program instructions operable to cause the processor to: average the plurality of predicted component concentrations to obtain an average predicted component concentration for each of the components of the target mixture (determining mean absolute error (MAE) and R2; FIG 11; [0099]).
Claims 7 and 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Angulo et al. (US Pub # 2023/0197207, e.f.d. 12/16/21) in view of Chen et al (US Pub # 2023/0393060) in view of Li et al.1
Regarding claims 7 and 20, Angulo does not specify wherein the mid-IR sensor comprises a mid-IR waveguide sensor comprising a subwavelength grating metamaterial waveguide formed on a substrate. In the same field of endeavor, Li discloses a mid-IR waveguide sensor comprising a subwavelength grating metamaterial waveguide formed on a substrate (section III; page 1, column 2; FIG 1), with the benefit of increased sensitivity in the mid-IR spectrum with specific application for sensing chemicals (section I, page 1, column 1). In light of the teachings of Li, it would have been obvious to one of ordinary skill in the art at the time of the invention to combine with the teachings of Angulo.
Regarding claim 8, Li discloses the periodic arrangement as less than or equal to 800 nm (fixed period of 500 nm; page 2, column 1; Figure 3; TABLE 1).
Regarding claim 10, Li discloses wherein the target absorption spectrum is measured in a mid-infrared wavelength range of 3.35-3.45 µm. (section III; page 1, col. 2), but does not specify wherein the wavelength range is 3.7 µm to 3.8 µm. However, Li explains that “It is well-known that mid-infrared (mid-IR) region possesses most fundamental vibration signatures of almost all chemical bonds, which have orders of magnitude larger absorption cross-sections than the overtones in the near-IR. Therefore, by moving from near-IR to MIR, the sensitivity of detection can be enhanced by several orders”. Indeed, aldehydes, acetylene, are hydrocarbons all known to possess strong absorption characteristics in the 3.7 to 3.8 µm range. Thus, it would have been obvious to one of ordinary skill in the art at the time of the invention to choose a wavelength band of 3.7-3.8 µm, with the benefit of applying chemical decomposition to a variety compounds.
Claim 18 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Angulo et al. (US Pub # 2023/0197207, e.f.d. 12/16/21) in view of Chen et al (US Pub # 2023/0393060) in view of Scott et al. (US Pub # 2022/0091026).
Regarding claim 18, Angulo does not specify wherein the method further comprises normalizing the first training absorption spectra and the target absorption spectrum with a buffer absorption spectrum of a buffer solution, the buffer solution being the buffer used in the plurality of training mixtures and the target mixture. In the same field of endeavor, Scott discloses a method of spectral decomposition comprising the step of normalizing a first training absorption spectra and a target absorption spectrum with a buffer absorption spectrum of a buffer solution, the buffer solution being the buffer used in the plurality of training mixtures and the target mixture, with the benefit of lower noise and improved accuracy (sample spectrum normalized against a calibrated compound mixture; [0189, 0193]). In light of the teachings of Scott, it would have been obvious to one of ordinary skill in the art at the time of the invention to combine with the teachings of Angulo.
Allowable Subject Matter
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Regarding claim 9, the prior art fails to disclose or suggest, in combination with the other claimed elements, wherein the index of a propagation mode of the mid-IR waveguide sensor is higher than a refractive index of the substrate.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CASEY BRYANT whose telephone number is (571)270-7329. The examiner can normally be reached M-F // 7-3P EST.
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CASEY BRYANT
Primary Examiner
Art Unit 2884
/CASEY BRYANT/Primary Examiner, Art Unit 2884
1 T. Li, P. Zhou, Y. Wu, S. Tang and Y. Zou, "Mid-Infrared Air Top-Cladded Subwavelength Grating Waveguides," 2019 IEEE Photonics Conference (IPC), San Antonio, TX, USA, 2019, pp. 1-2, doi: 10.1109/IPCon.2019.8908392.