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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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)(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) 1-14, 17-21, 23-27, is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vohra et al. (US 2021/0231499 A1) [hereinafter Vohra].
The applied reference has a common, MKS Technology, Inc., Mark, Watson, with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
As to claim 1, Vohra teaches a method of adjusting optical data comprising: receiving optical data from a detector (48, Fig.1, paragraphs 0057, 0066), wherein the optical data comprises an optical signal and at least one baseline component in the optical data (paragraph 0099, claim 37); generating a plurality of shifted optical data sets (paragraph 0017), separating data corresponding to the optical signal and the at least one component (paragraph 0098, Fig.9B), and determining the at least one component of the optical data based on the separated optical signal (paragraph 0088).
As to claim 2, Vohra teaches all as applied to claim 1, and in addition teaches, wherein the operation of separating data comprises separating the optical data corresponding to the optical signal from the optical data corresponding to at least one component (paragraph 0098, Fig.9B) by: superimposing data from the optical data obtained from the detector and the plurality of shifted optical data sets to create a vector (0097, Fig.9A), and building a mathematical matrix operator (paragraph 0097, Fig.9A), and solving to determine an optical signal and a vector corresponding to the at least one component (paragraph 0097, Fig.9A).
As to claim 3, Vohra teaches all as applied to claim 1, and in addition teaches, wherein the optical data comprises one or more selected from the group comprising: x, y data, x, y, z data, spectroscopic data, chromatographic data, image data, and video image data (note spectroscopic data disclosed, paragraph 0066, the spectrometer comprises an optical system adapted to receive a spectroscopic signal).
As to claim 4, Vohra teaches all as applied to claim 1, and in addition teaches, wherein the baseline component comprises at least one of the group comprising an optical aberration, fluorescence, noise, and an interference pattern (paragraph 0022).
As to claim 5, Vohra teaches all as applied to claim 1, and in addition teaches, wherein the plurality of data shifts comprises a plurality of relatively constant data shifts (paragraph 0103, Fig.11).
As to claim 6, Vohra teaches all as applied to claim 1, and in addition teaches, wherein the plurality of data shifts comprises a plurality of constant data shifts (paragraph 0104, Fig.12).
As to claim 7, Vohra teaches all as applied to claim 1, and in addition teaches, wherein the plurality of data shifts comprises at least two sets of dynamic data shifts (paragraphs 0017, 0059).
As to claim 8, Vohra teaches an image processing device comprising: an image detector (48, Fig.1); and a controller (52, Fig.1) adapted to: receive optical data from a detector, wherein the optical data comprises an optical signal and at least one baseline component in the optical data (paragraph 0099, claim 37) , generate a plurality of shifted optical data sets (paragraph 0017), separate data corresponding to the optical signal and the at least one component (paragraph 0098, Fig.9B), and determine the at least one component of the optical data based on the separated optical signal (paragraph 0088).
As to claim 9, Vohra teaches all as applied to claim 8, and in addition teaches, wherein the baseline component comprises at least one of the group comprising an optical aberration, fluorescence, noise, and an interference pattern (paragraph 0022).
As to claim 10, Vohra teaches all as applied to claim 8, and in addition teaches, wherein the plurality of data shifts comprises a plurality of relatively constant data shifts (paragraph 0103, Fig.11).
As to claim 11, Vohra teaches all as applied to claim 8, and in addition teaches, wherein the plurality of data shifts comprises a plurality of constant data shifts (0104, Fig.12).
As to claim 12, Vohra teaches all as applied to claim 8, and in addition teaches, wherein the plurality of data shifts comprises at least two sets of dynamic data shifts (paragraphs 0017, 0059).
As to claim 13, Vohra teaches a spectrometer comprising: a laser (22, Fig.1) adapted to provide an excitation beam (paragraph 0052); an optical system adapted to provide the excitation beam to a sample and receive a Raman scattering signal from the sample (20, Fig.1, paragraph 0051); a detector (48, Fig.1, paragraph 0057) adapted to receive the Raman scattering signal via the optical system; and a controller (52, Fig.1) adapted to read a first set of sample spectral data from the detector, the first set of sample spectral data comprising a single measured Raman spectrum component based on the Raman scattering signal and a baseline component (paragraph 0099, claim 37), generate a plurality of sets of shifted spectral data corresponding to the first set of sample spectral data of the single measured Raman spectrum (paragraph 0104, Fig.12), separate data corresponding to the measured Raman spectrum and the baseline component from the first set of sample spectral data (paragraph 0098, Fig.9B), and determine at least one component of the sample based on the separated measured single Raman spectrum (paragraphs 0098, 0088, Fig.9B).
As to claim 14, Vohra teaches all as applied to claim 13, and in addition teaches, wherein the baseline component comprises at least one of noise, fluorescence, and an interference pattern (paragraph 002).
As to claim 17, Vohra teaches all as applied to claim 13, and in addition teaches, wherein the controller (52, Fig.1, paragraph 0064) is adapted to separate the spectral data corresponding to the single Raman spectrum from the spectral data corresponding to at least one of noise, fluorescence, and an interference pattern (paragraph 0098, Fig. 9B) by: superimposing data from the spectral data obtained from the detector and the plurality of shifted spectral data to create a vector paragraph 0097, Fig.9A), and building a mathematical matrix operator, and solving to determine a Raman signal and a vector corresponding to at least one of noise, fluorescence, and an interference pattern (paragraph 0097, Fig.9A).
As to claim 18, Vohra teaches all as applied to claim 13, and in addition teaches, wherein the spectral data comprises one or more of the group comprising: pixel data, wavenumber data, wavelength data, and energy data (paragraph 0102, Fig.10).
As to claim 19, Vohra teaches all as applied to claim 13, and in addition teaches, wherein the plurality of data shifts comprises a plurality of relatively constant data shifts (paragraph 0103, Fig.11).
As to claim 20, Vohra teaches all as applied to claim 13, and in addition teaches, wherein the plurality of data shifts comprises a plurality of constant data shifts (paragraph 0104, Fig.12).
As to claim 21, Vohra teaches all as applied to claim 13, and in addition teaches, wherein the plurality of data shifts comprises at least two sets of dynamic data shifts (paragraphs 0017, 0059).
As to claim 23, Vohra teaches a method of determining a Raman spectroscopic signal (abstract), the method comprising: obtaining a single measured spectrum (paragraph 0098, Fig.9B); generating a plurality of shifted, discrete spectra corresponding to the single measured spectrum (paragraph 0102, Fig.10); superimposing data from each of the plurality of discrete shifted spectroscopic signals to create a vector (paragraph 0097, Fig.9A); building a mathematical matrix operator; and using the mathematical matrix operator to solve a linear problem (paragraph 0097, Fig.9A).
As to claim 24, Vohra teaches all as applied to claim 23, and in addition teaches, wherein the method comprises using an iterative method to solve the linear problem that determines a Raman signal (paragraph 0097, Fig.9A).
As to claim 25, Vohra teaches all as applied to claim 23, and in addition teaches, wherein the iterative method is further used to solve for an unknown vector of noise (paragraph 0097, Fig.9A).
As to claim 26, Vohra teaches all as applied to claim 23, and in addition teaches, wherein the noise comprises at least one of fluorescence and background radiation (paragraph 0021).
As to claim 27, Vohra teaches all as applied to claim 23, and in addition teaches, wherein the mathematical matrix operator represents identity matrices of non-shifted noise spectral elements from the plurality of discrete shifted spectroscopic signals (paragraph 0023).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 15-16, 22, is/are rejected under 35 U.S.C. 103 as being unpatentable over Vohra in view of LOS GATOS RESEARCH (hereinafter GATOS).
As to claim 15, Vohra teaches all as applied to claim 13, except wherein the baseline component is relatively stable compared to the Raman spectrum over a range of wavenumbers in the first set of sample spectral data. However, GATOS in the same field of endeavor, teaches the baseline component is relatively stable over a range of wavenumbers (note. See Fig.2 for a range of wavenumbers, paragraph 0002; the absorption baselines is highly reproducible and stable improving the accuracy of multivariate fits; paragraphs 0010, Fig.2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to provide the baseline component that is relatively stable over a range of wavenumbers of GATOS with spectrometer of MKS, to improve accuracy (see GATOS, paragraph 0004).
Claim(s) 16, 22, is/are rejected under 35 U.S.C. 103 as being unpatentable over Vohra
As to claim 16, Vohra teaches all as applied to claim 13, except wherein the Raman signal comprises a resonance Raman signal. However, examiner takes Official Notice that producing Raman signal comprising resonance Raman signal is known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to produce Raman signal comprising resonance Raman signal in Raman system in order to enhance signal intensity due the resonance effect.
As to claim 22, Vohra teaches all as applied to claim 13, except wherein the spectrometer comprises a portable, handheld spectrometer. However, examiner takes Official Notice that providing portable spectrometer in optical apparatus is known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to provide portable spectrometer in optical apparatus because portable spectrometers are compact and lightweight and easily transportable to be used in various settings.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Wang et al. (US 2006/0197947 A1) teaches a data set processing method for Raman spectroscopy systems using tunable lasers and multielement spectrometers compiles the spectral data set into an array and then estimates the background component, which is usually dominated by sample and optical train fluorescence, detector array dark current signal, fixed-pattern signal, and stray-light signals either modulated or non-modulated by in-path optics. This estimate is used as a baseline correction to the spectral data set to thereby isolate the sample's Raman response (abstract).
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/ABDULLAHI NUR/Primary Examiner, Art Unit 2886