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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1, 4-5, 8, 12, 16 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ji et al. (CN 108896511 A, included in IDS on 02/26/2025), hereafter Ji.
Regarding claim 1, Ji teaches a method of mitigating distortion of an optical emission spectrum [page 2, lines 43-51] obtained from an optical emission spectrometer, (emission monitoring from a spectrum analysis instrument, that is a spectrometer, [page 2, lines 1-10, page 3, line 49]) wherein the optical emission spectrometer comprises an optical system for forming the optical emission spectrum [page 2, lines 1-10],, the method comprising:
obtaining a spectrum recorded with the optical emission spectrometer (“The TDLAS analyzer 40 collects the near-infrared absorption spectrum of the standard sample gas. “, [page 4, lines 24-41]) and input data comprising one or more condition parameters indicative of an operating condition of the optical emission spectrometer or an environment of the optical emission spectrometer at a time of recording the spectrum with the optical emission spectrometer, (hardware parameters, [page 2, lines 53-55 to page 3, lines 1-9], [page 5, 13-19],
providing a machine learning model configured to output, in response to the input data, (deformation analysis model is established based on (ELM), (KELM), [page 3, lines 11-20], output data comprising one or more transform parameters of a transformation to be applied to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and a baseline operating condition, (the analyzer perform calibration by comparing the reference spectrum at the factory and by applying the model to calculate deformation coefficient to correct software and hardware parameters, [page 3, lines 29-36];
applying the input data (absorption spectrum, [page 4, lines 24-27]) as an input to the machine learning model and obtaining one or more transform parameters as an output of the machine learning model, (deformation coefficients, [page 3, lines 33-36, 51-52]);
and
applying the transformation in accordance with the obtained one or more transform parameters to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and the baseline operating condition, (according to deformation coefficient the parameters are corrected, so that the spectrum of the standard sample on the site coincides with the spectrum of the standard sample at the factory to achieve the purpose of repair. [page 5, lines 13-31, 47-51]).
Regarding claim 4, Ji teaches the method of claim 1, wherein the one or more condition parameters comprise a parameter indicative of a temperature, [page 3, lines 11-20], [page 4, lines 1-4].
Regarding claim 5, Ji teaches the method of claim 1, wherein the one or more condition parameters are indicative of an operating condition of the optical system of the optical emission spectrometer, (center wavelength and wavelength sweep range of the laser, current drive parameters of the laser, current modulation parameter of the laser [page 3, lines 33-36], [page 4, 45-55]).
Regarding claim 8, Ji teaches the method of claim 1, wherein the one or more condition parameters, [page 3, lines 33-36], [page 4, 45-55] comprises one or more condition change parameters indicative of a change of the operating condition or a direction of change of the operating condition, (ΔIs, ΔIf, ΔG, ΔB, [page 5, lines 13-19]).
Regarding claim 12, Ji teaches the method of claim 1, wherein the transformation comprises one or more of: a translation; a rotation; a scaling operation; a skewing operation, a stretching operation, [page 2, lines 49-51]; or a deformation field, [page, 3, lines 1-10].
Regarding claim 16, Ji teaches one or more non-transitory computer readable media comprising instructions thereon that, when executed by one or more processing devices of a scientific instrument support apparatus, [page 1, lines 28-32], [page 2 , lines 1-4] , cause the scientific instrument support apparatus to:
obtain a spectrum recorded with an optical emission spectrometer (emission monitoring from a spectrum analysis instrument, that is a spectrometer, [page 2, lines 1-10, page 3, line 49], (“The TDLAS analyzer 40 collects the near-infrared absorption spectrum of the standard sample gas. “, [page 4, lines 24-41]) and input data comprising one or more condition parameters indicative of an operating condition of the optical emission spectrometer or an environment of the optical emission spectrometer at a time of recording the spectrum with the optical emission spectrometer (hardware parameters, [page 2, lines 53-55 to page 3, lines 1-9], [page 5, 13-19],;
apply the input data (absorption spectrum, [page 4, lines 24-27]) as an input to a machine learning model that is configured to output, in response to the input data (deformation coefficients, [page 3, lines 33-36, 51-52]), output data comprising one or more transform parameters of a transformation to be applied to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and a baseline operating condition, (deformation analysis model is establish based on (ELM), (KELM), [page 3, lines 11-20], (according to deformation coefficient the parameters are corrected, so that the spectrum of the standard sample on the site coincides with the spectrum of the standard sample at the factory to achieve the purpose of repair. [page 5, lines 13-31, 47-51])
obtain one or more transform parameters as an output of the machine learning model, [page 5, lines 13-19, 43-35]; and
apply the transformation in accordance with the obtained one or more transform parameters to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and the baseline operating condition, (according to deformation coefficient the parameters are corrected, so that the spectrum of the standard sample on the site coincides with the spectrum of the standard sample at the factory to achieve the purpose of repair. [page 5, lines 13-31, 47-51]).
Regarding claim 20, A method of obtaining training data for training a machine learning model, the method comprising:
recording a plurality of optical emission spectra of a reference analyte, standard sample gas collected by TDLAS, page 4, lines 29-41], with an optical emission spectrometer for respective operating conditions of the optical emission spectrometer ([page 2, lines 1-10, page 3, line 49], (ΔIs, ΔIf, ΔG, ΔB, [page 5, lines 13-19]);
storing input data for the machine learning model, the input data comprising, for each recorded optical emission spectrum, one or more parameters indicative of the respective operating condition (deformation analysis model is established based on (ELM), (KELM), [page 3, lines 11-20],;
for each recorded optical emission spectrum, adjusting one or more transform parameters of a transform to register the optical emission spectrum to a baseline optical emission spectrum of the reference analyte using the transform, wherein the baseline optical emission spectrum was recorded for a baseline operating condition, (the analyzer perform calibration by comparing the reference spectrum at the factory and by applying the model to calculate deformation coefficient to correct software and hardware parameters, [page 3, lines 29-36]; , [page 5, lines 33-45]; and
storing output data for the machine learning model comprising, for each optical emission spectrum, the respective adjusted one or more transform parameters in association with the respective input data for each optical emission spectrum as a training data pair, (ensure calibration model is till applicable based on deformation coefficients, [page 5, lines 47-51].
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-3, 15, 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Ji in view of SCHLUETER et al. (WO 2021018992 A1, included in IDS on 02/26/2025), hereafter Schlueter.
Regarding claims 2-3, 15 and 21, Ji teaches the method of claim 1.
Ji is silent about:
(claim 2) wherein the spectrum comprises sets of intensity values over respective two-dimensional locations, and wherein the transformation comprises an operation that varies across locations.
(claim 3) wherein the operation comprises applying a deformation field to the optical emission spectrum.
(claim 15) wherein the spectrum is an echelle spectrum.
(claim 21) wherein the optical emission spectrum and the baseline optical emission spectrum each comprise sets of intensity values over respective two-dimensional locations, and wherein the transform comprises an operation that varies across multiple locations.
However, Schlueter related to spectrum analyzer and thus from the same field of endeavor teaches:
(claim 2) wherein the spectrum comprises sets of intensity values over respective two-dimensional locations, (generating a two-dimensional array of the spectrum values of the locations, [page 2, lines 30-35 to page 3, lines 1-11), and wherein the transformation comprises an operation that varies across locations, (interpolations of the spectra in each first and second intensity peak, [page 2, lines 30-35 to page 3, lines 1-11).
(claim 3) wherein the operation comprises applying a deformation field to the optical emission spectrum, (interpolations of the spectra in each first and second intensity peak, [page 2, lines 30-35 to page 3, lines 1-11).
(claim 15) wherein the spectrum is an echelle spectrum, [page 8, lines 9-14].
(claim 21) wherein the optical emission spectrum and the baseline optical emission spectrum each comprise sets of intensity values over respective two-dimensional locations, (generating a two-dimensional array of the spectrum values of the locations, [page 2, lines 30-35 to page 3, lines 1-11), and wherein the transform comprises an operation that varies across multiple locations, (interpolations of the spectra in each first and second intensity peak, [page 2, lines 30-35 to page 3, lines 1-11).
Therefore, it would been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ji by including wherein the spectrum comprises sets of intensity values over respective two-dimensional locations, and wherein the transformation comprises an operation that varies across locations, wherein the operation comprises applying a deformation field to the optical emission spectrum, wherein the spectrum is an echelle spectrum, wherein the optical emission spectrum and the baseline optical emission spectrum each comprise sets of intensity values over respective two-dimensional locations, and wherein the transform comprises an operation that varies across multiple locations (as taught by Schlueter) for several advantages such as: interpolation allows determining the peak intensity with greater accuracy, especially when the peak is only a few pixels wide. Any rounding errors can be reduced by using sub-pixel interpolation, while the effect of drift on rounding errors is significantly reduced, thus increase the device accuracy, ([page 5, lines 24-30], Schlueter).
Regarding claim 23, Ji in the combination about lined above teaches the method of claim 21.
Ji further teach wherein the optical emission spectrum and the baseline optical emission spectrum are respective images and adjusting the one or more transform parameters comprises comparing respective image intensities between the respective images, (the analyzer perform calibration by comparing the reference spectrum at the factory and by applying the model to calculate deformation coefficient to correct software and hardware parameters, [page 3, lines 29-36]; , [page 5, lines 33-45]).
Claims 6-7, 9 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Ji in view of Kamio S. et al. : "Modelling of EIS spectrum drift from instrumental temperatures", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 20i OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 18 March 2010, hereafter Kamio, included in IDS on 02/26/2025).
Regarding claims 6-7, 9 and 13-14, Ji teaches the method.
Even though Ji teaches wherein the one or more condition parameters comprise a parameter indicative of a temperature, [page 3, lines 11-20], [page 4, lines 1-4], Ji is silent about:
(claim 6) wherein the one or more condition parameters comprise at least one temperature measurement obtained from a temperature sensor attached to a mechanical structure of the optical emission spectrometer.
(claim 7) wherein the mechanical structure of the optical emission spectrometer supports one or more optical components of the optical system.
(claim 9) wherein the input data comprises a time series of the one or more condition parameters at each of a plurality of time points.
(claim 13) wherein the one or more condition parameters comprise one or more of:
a parameter indicative of a heating current applied to a heating arrangement for heating and stabilizing a temperature of the optical system;
a parameter indicative of a temperature of an environment of the optical emission spectrometer;
a parameter indicative of the temperature of the optical system;
a parameter indicative of at least one temperature measurement obtained from a temperature sensor attached to a mechanical structure supporting one or more optical components of the optical system;
a parameter indicative of an RF power of an RF generator for generating a plasma for use in obtaining the spectrum; or
a parameter indicative of an exhaust pressure of a plasma chamber for containing a plasma for use in obtaining the spectrum.
(claim 14) wherein the optical emission spectrometer is a plasma emission spectrometer configured to record an emission spectrum of light emitted from a plasma.
However, Kamio related to modification of spectral pattern changes of spectroscopic devices and thus from the same field of endeavor teaches:
(Claim 6) wherein the one or more condition parameters comprise at least one temperature measurement obtained from a temperature sensor (temperature sensor) attached to a mechanical structure of the optical emission spectrometer (Fig. 1 element EIS), [page 210, section 2, first paragraph]).
(claim 7) wherein the mechanical structure of the optical emission spectrometer supports one or more optical components of the optical system, [page 210, section 2, first paragraph]).
(claim 9) wherein the input data comprises a time series of the one or more condition parameters at each of a plurality of time points, (the spectrum position of the temperature is determined in a time series as shown in fig. 2, [pages 210-211, section 2, first and second paragraphs]).
(claim 13) wherein the one or more condition parameters comprise one or more of:
a parameter indicative of a heating current applied to a heating arrangement for heating and stabilizing a temperature of the optical system;
a parameter indicative of a temperature of an environment of the optical emission spectrometer; [page 210, section 2, first paragraph]
a parameter indicative of the temperature of the optical system; , [page 210, section 2, first paragraph]
a parameter indicative of at least one temperature measurement obtained from a temperature sensor attached to a mechanical structure supporting one or more optical components of the optical system;
a parameter indicative of an RF power of an RF generator for generating a plasma for use in obtaining the spectrum; or
a parameter indicative of an exhaust pressure of a plasma chamber for containing a plasma for use in obtaining the spectrum.
(claim 14) wherein the optical emission spectrometer is a plasma emission spectrometer configured to record an emission spectrum of light emitted from a plasma, [page 209, second paragraph].
Therefore, it would been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ji by including wherein the one or more condition parameters comprise at least one temperature measurement obtained from a temperature sensor attached to a mechanical structure of the optical emission spectrometer, wherein the input data comprises a time series of the one or more condition parameters at each of a plurality of time points, wherein the one or more condition parameters comprise one or more of: a parameter indicative of a heating current applied to a heating arrangement for heating and stabilizing a temperature of the optical system; a parameter indicative of a temperature of an environment of the optical emission spectrometer; a parameter indicative of the temperature of the optical system; a parameter indicative of at least one temperature measurement obtained from a temperature sensor attached to a mechanical structure supporting one or more optical components of the optical system; a parameter indicative of an RF power of an RF generator for generating a plasma for use in obtaining the spectrum; or a parameter indicative of an exhaust pressure of a plasma chamber for containing a plasma for use in obtaining the spectrum, wherein the mechanical structure of the optical emission spectrometer supports one or more optical components of the optical system, wherein the optical emission spectrometer is a plasma emission spectrometer configured to record an emission spectrum of light emitted from a plasma, (as taught by Kamio) for several advantages such as: the measurement of the temperature distribution of the device in time intervals, allows to develop a model to account for their time derivatives to deduce an empirical relationship between instrumental temperatures and spectral drift from the huge data set, as would increase the device accuracy, ([page 210-211, section method, first and second paragraph], Kamio).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Ji in view of In view of Vaez-Iravani et al. (US 20230169643 A1), hereafter Vaez.
Regarding claim 11, Ji teaches the method of claim 1.
Even though Ji teaches a machine learning model wherein the machine learning model, Ji fail to teach a decision-tree based ensemble machine learning algorithm.
However, Vaez, related to spectral analysis of a spectrometer with machine learning and thus from the same field of endeavor teaches a decision-tree based ensemble machine learning algorithm, [0050].
Therefore, it would been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ji by including a decision-tree based ensemble machine learning algorithm, (as taught by Kamio) for several advantages such as: allows to increase the prediction accuracy of the device.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Ji in view of SCHLUETER and in further view of Valdes et al. (US 11,510,600 B2), hereafter Valdes.
Regarding claim 22, Ji in the combination outlined above teaches the method of claim 21.
The modified method of Ji fail to teach wherein the operation comprises applying a distortion field to the optical emission spectrum.
However, Valdes related to spectral analysis by neural networks and thus from the same field of endeavor teaches wherein the operation comprises applying a distortion field to the optical emission spectrum, [col. 22, lines 20-50].
Therefore, it would been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the modified device of Ji by including wherein the operation comprises applying a distortion field to the optical emission spectrum, (as taught by Valdes) for several advantages such as: allows to constrain a mechanical model of the test object, thus the location of the object can be located and display, thus increase the device efficiency and accuracy, ([col. 22, lines 55-63], Valdes).
Allowable Subject Matter
Claim 10 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.
Regarding Claim 10, the prior art of record, taken either alone or in combination, fails to disclose, teach, or suggest or render obvious “wherein the output data comprises a corresponding time series of one or more transform parameters of a transformation at each of the plurality of time points, and wherein the method comprises applying the transformation in accordance with the one or more transform parameters corresponding to a time point of the time series of the output data to the optical emission spectrum obtained at the time point.”, in the combination required by the claim.
Conclusion
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/CARLOS PEREZ-GUZMAN/ Examiner, Art Unit 2877
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