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 Objections
Claim 1 is objected to because of the following informalities: in line 9, it appears that “a calibration model” should perhaps be “a first calibration model” since it is referred to that way in line 19. Appropriate correction is required.
Claim 10 is objected to because of the following informalities: in lines 2-3, it appears that “a sample” should be “a first sample”. Appropriate correction is required.
Claim 10 is objected to because of the following informalities: in line 4, it appears that “a spectroscopic instrument” should be “a first spectroscopic instrument”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claims 4, 10, and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites, in line 2, “using the calibration model to output an amount…” It is unclear which calibration model is being referred to, the calibration model recited in line 9 or the calibration model recited in line 17 of claim 1.
Claim 10 recites, in lines 8-10, “generating a calibration model relating a plurality of spectroscopic intensities at a corresponding plurality of wavelengths to an amount of the chemical element in a sample based on training a machine-learning model using the calibration data”. Lines 18-19 recite, “wherein the calibration model is generated based on training the machine-learning model using the second calibration data.” There appears to be a typo because it is unclear how the calibration model is generated. It appears that the calibration model is generated based on training a machine-learning model using the (first) calibration data. Then second calibration data is received. The calibration model is then recited to be based on training the machine-learning model using the second calibration data. It is not clear if this is another calibration model or the same calibration model. The metes and bounds are indeterminable.
Claims 11-17 are rejected by virtue of their dependency on claim 10.
Claim 14 appears to recite part of the recitations which appear in claim 10. It is not clear what exactly is being claimed.
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 18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sun et al. (“Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soils with Generalized LIBS Spectra”, Scientific Reports, 2019, Vol. 9, No. 11363, 18 pages, Retrieved from the internet: URL: https://www.nature.com/articles/s41598-019-47751-y) – copy provided by applicant.
With respect to claim 18, Sun et al. disclose a method of supporting spectroscopic calibration (page 2 - establishing independent calibration models for spectroscopy), comprising: receiving calibration data, wherein the calibration data includes an amount of a chemical element in a sample and a plurality of spectroscopic intensities, associated with a corresponding plurality of wavelengths (page 7 - the concentration [and thus amount] of Ag may be determined from the calibration model, and the individual spectra of the Ag may be used in the calibration file), generated by spectroscopic analysis of the sample by a spectroscopic instrument (page 12 - Ag solutions in soils were analyzed spectroscopically); and generating a target calibration model for the spectroscopic instrument relating a plurality of spectroscopic intensities at a corresponding plurality of wavelengths to an amount of a chemical element in a sample based on training a machine-learning model (pages 14-15 - the calibration models [for the instrument] may be used to create a target calibration model via machine learning), based on a base machine-learning model, using the calibration data for the spectroscopic instrument, wherein the base machine-learning model is trained on calibration data from a plurality of spectroscopic instruments different from the spectroscopic instrument (page 13 - a base calibration model may be used to help train further calibration models, where the base model is trained off of calibration data from multiple spectroscopic instruments).
With respect to claim 20, Sun et al. disclose further comprising: using the target calibration model to output an amount of the chemical element in a sample-under-test based on a plurality of spectroscopic intensities (pages 3 & 14-15 - the concentration [and thus the amount] of Ag may be measured with the target calibration model), associated with a corresponding plurality of wavelengths, generated by spectroscopic analysis of the sample-under-test (page 7 - the individual spectra of the Ag may be used in the calibration file).
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.
Claims 1-3, 5-7, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Chatzidakis et al. (“Towards calibration-invariant spectroscopy using deep learning”, Scientific Reports, 2019, Vol. 9, No. 2126, 10 pages, retrieved from the internet: URL:https://www.nature.com/articles/s41598-019-38482-1) - copy provided by applicant - in view of Lightner et al. (US 2011/0125477 A1).
With respect to claim 1, Chatzidakis et al. disclose a method of supporting spectroscopic calibration (abstract - an automated feature to support spectroscopic calibration), comprising: receiving first calibration data (page 2 - investigating the valence identification of multiple [including a first] sets of Mn data), generated by spectroscopic analysis of the first sample by a first spectroscopic instrument (page 2 - data sets for various ions of Mn were selected from various published studies); receiving second calibration data (page 2 - investigating the valence identification of multiple [including a second] sets of Mn data), generated by spectroscopic analysis of the second sample by a second spectroscopic instrument, and wherein the second spectroscopic instrument is different from the first spectroscopic instrument (page 2 - data sets for various ions of Mn were selected from various published studies [using different spectroscopic instruments]); receiving third calibration data (page 2 - investigating the valence identification of multiple [including a third] sets of Mn data), generated by spectroscopic analysis of the third sample by a third spectroscopic instrument, and wherein the third spectroscopic instrument is different from the first spectroscopic instrument and from the second spectroscopic instrument (page 2 - data sets for various ions of Mn were selected from various published studies [using different instruments]); and generating a second calibration model based on the first calibration model, using the third calibration data (pages 3 & 7 - multiple calibration models are generated based upon different sections [including the third section] of the data, and the calibration models may include previous calibration models to train off of). Chatzidakis et al. fail to disclose wherein the first calibration data includes an amount of a chemical element in a first sample and a plurality of spectroscopic intensities, associated with a corresponding plurality of wavelengths, wherein the second calibration data includes an amount of the chemical element in a second sample and a plurality of spectroscopic intensities, associated with a corresponding plurality of wavelengths, generating a calibration model relating a plurality of spectroscopic intensities at a corresponding plurality of wavelengths to an amount of the chemical element in a sample based on training a machine-learning model using the first calibration data and the second calibration data; wherein the third calibration data includes an amount of the chemical element in a third calibration sample and a plurality of spectroscopic intensities, associated with a corresponding plurality of wavelengths, and relating a plurality of spectroscopic intensities at a corresponding plurality of wavelengths to an amount of the chemical element in a sample by training a machine-learning model. Lightner et al. disclose wherein the first calibration data includes an amount of a chemical element in a first sample and a plurality of spectroscopic intensities, associated with a corresponding plurality of wavelengths (paragraph 0089 - spectroscopic data includes wavelengths, intensities, as well as an amount of the sample), wherein the second calibration data includes an amount of the chemical element in a second sample and a plurality of spectroscopic intensities, associated with a corresponding plurality of wavelengths (paragraph 0089 - spectroscopic data includes wavelengths, intensities, as well as an amount of the sample), generating a calibration model relating a plurality of spectroscopic intensities at a corresponding plurality of wavelengths to an amount of the chemical element in a sample based on training a machine-learning model using the first calibration data and the second calibration data (paragraphs 0062-0063 - a neural network is used to generate a calibration model based on spectroscopic data including wavelength and intensity [from first and second calibration data]); wherein the third calibration data includes an amount of the chemical element in a third calibration sample and a plurality of spectroscopic intensities, associated with a corresponding plurality of wavelengths (paragraph 0089 - spectroscopic data includes wavelengths, intensities, as well as an amount of the sample), and relating a plurality of spectroscopic intensities at a corresponding plurality of wavelengths to an amount of the chemical element in a sample by training a machine-learning model, (paragraph 0426 - a neural network may be used to correlate spectroscopic data [intensities at wavelengths to an amount of chemical present]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Chatzidakis et al. to include specific spectroscopic data, as taught by Lightner et al., in order to properly train the calibration models.
With respect to claim 2, Chatzidakis et al./Lightner et al. disclose the method of claim 1. Chatzidakis et al. further discloses wherein a number of the plurality of wavelengths, in the first calibration data is less than a total number of wavelengths with associated spectroscopic intensities output by the first spectroscopic instrument during spectroscopic analysis of the first sample (page 3 - the data [Including the wavelengths] may be sectioned into portions, where no portion may encompass the entire [first] dataset). Chatzidakis et al. fail to disclose wavelengths associated with the corresponding plurality of spectroscopic intensities. Lightner et al. disclose wavelengths associated with the corresponding plurality of spectroscopic intensities (paragraph 0089 - spectroscopic data includes wavelengths, intensities, as well as an amount of the sample). It would have been obvious to one of ordinary skill in the art at the time the invention was made to further modify Chatzidakis et al. to include correlated data, as taught by Lightner et al., in order to provide a full set of data for training.
With respect to claim 3, Chatzidakis et al./Lightner et al. disclose the method of claim 1. Chatzidakis et al. further disclose wherein a number of the plurality of wavelengths, in the second calibration data is less than a total number of wavelengths with associated spectroscopic intensities output by the second spectroscopic instrument during spectroscopic analysis of the second sample (page 3 - the data [including the wavelengths] may be sectioned Into portions, where no portion may encompass the entire [second] dataset). Chatzidakis et al. fail to disclose wavelengths associated with the corresponding plurality of spectroscopic intensities. Lightner et al. disclose wavelengths associated with the corresponding plurality of spectroscopic intensities (paragraph 0089 - spectroscopic data includes wavelengths, intensities, as well as an amount of the sample). It would have been obvious to one of ordinary skill in the art at the time the invention was made to further modify Chatzidakis et al. to include correlated data, as taught by Lightner et al., in order to provide multiple full data sets for training.
With respect to claim 5, Chatzidakis et al./Lightner et al. disclose the method of claim 1. Chatzidakis et al. further disclose wherein the third sample has a same material composition as the second sample (page 2 - data sets [second and third] for various ions of Mn were selected from various published studies).
With respect to claim 6, Chatzidakis et al./Lightner et al. disclose the method of claim 1. Chatzidakis et al. fail to disclose wherein the third sample has a different material composition than the second sample. Lightner et al. disclose wherein the third sample has a different material composition than the second sample (paragraph 0118 - different plant species may be used to train the correlation models [for second and third data sets]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to further modify Chatzidakis et al. to include multiple compositions of samples, as taught by Lightner et al., in order to provide varied data sets for model training.
With respect to claim 7, Chatzidakis et al./Lightner et al. disclose the method of claim 1. Chatzidakis et al. further disclose wherein a number of the plurality of wavelengths, in the third calibration data is less than a total number of wavelengths with associated spectroscopic intensities output by the third spectroscopic instrument during spectroscopic analysis of the third sample (page 3 - the data [including the wavelengths] may be sectioned into portions, where no portion may encompass the entire [third] dataset). Chatzidakis et al. fail to disclose wavelengths associated with the corresponding plurality of spectroscopic intensities. Lightner et al. disclose wavelengths associated with the corresponding plurality of spectroscopic intensities (paragraph 0089 - spectroscopic data includes wavelengths, intensities, as well as an amount of the sample). It would have been obvious to one of ordinary skill in the art at the time the invention was made to further modify Chatzidakis et al. to include correlated data, as taught by Lightner et al., in order to provide a significant number of data sets for training.
With respect to claim 9, Chatzidakis et al./Lightner et al. disclose the method of claim 1. Chatzidakis et al. further disclose wherein an amount of the third calibration data used to generate the second calibration model is less than an amount of the first calibration data used to generate the first calibration model (page 8 - models [such as the second model] may be generated that rely upon a smaller subset of data).
Claims 4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Chatzidakis et al. (“Towards calibration-invariant spectroscopy using deep learning”, Scientific Reports, 2019, Vol. 9, No. 2126, 10 pages, retrieved from the internet: URL:https://www.nature.com/articles/s41598-019-38482-1) - copy provided by applicant - in view of Lightner et al. (US 2011/0125477 A1), as applied to claim 1 above, and further in view of Sun et al. (“Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soils with Generalized LIBS Spectra”, Scientific Reports, 2019, Vol. 9, No. 11363, 18 pages, Retrieved from the internet: URL: https://www.nature.com/articles/s41598-019-47751-y) – copy provided by applicant.
With respect to claim 4, Chatzidakis et al./Lightner et al. disclose the method of claim 1. Modified Chatzidakis et al. fail to disclose further comprising: using the calibration model to output an amount of the chemical element in a sample-under-test based on a plurality of spectroscopic intensities, associated with a corresponding plurality of wavelengths, generated by spectroscopic analysis of the sample-under-test. Sun et al. disclose further comprising: using the calibration model to output an amount of the chemical element in a sample-under-test based on a plurality of spectroscopic intensities (page 3 - the concentration [and thus the amount] of Ag may be measured with the calibration model), associated with a corresponding plurality of wavelengths, generated by spectroscopic analysis of the sample-under-test (page 7 - the individual spectra of the Ag may be used in the calibration file). It would have been obvious to one of ordinary skill in the art at the time the invention was made to further modify Chatzidakis et al./Lightner et al. to include determining amounts of elements, as taught by Sun et al., in order to provide sample amounts for test results.
With respect to claim 8, Chatzidakis et al./Lightner et al. disclose the method of claim 1. Chatzidakis et al. further disclose a second calibration model (page 2 - multiple machine learning architectures may be used to make multiple calibration models), and a third spectroscopic instrument (page 2 - data sets for various ions of Mn were selected from various published studies [using different instruments]). Modified Chatzidakis et al. fail to disclose further comprising: using the calibration model to output an amount of the chemical element in a sample-under-test based on a plurality of spectroscopic intensities associated with a corresponding plurality of wavelengths, generated by spectroscopic analysis of the sample-under-test by the spectroscopic instrument. Sun et al. disclose further comprising: using the calibration model to output an amount of the chemical element in a sample-under-test based on a plurality of spectroscopic intensities (page 3 - the concentration [and thus the amount] of Ág may be measured with the calibration model), associated with a corresponding plurality of wavelengths, generated by spectroscopic analysis of the sample-under-tèst by the spectroscopic instrument (page 7 - the individual spectra of the Ag may be used in the calibration file). It would have been obvious to one of ordinary skill in the art at the time the invention was made to further modify Chatzidakis et al./Lightner et al. to include spectroscopic data, as taught by Sun et al., in order to provide adequate data for model training.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (“Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soils with Generalized LIBS Spectra”, Scientific Reports, 2019, Vol. 9, No. 11363, 18 pages, Retrieved from the internet: URL: https://www.nature.com/articles/s41598-019-47751-y) – copy provided by applicant – as applied to claim 18 above, and further in view of Chatzidakis et al. (“Towards calibration-invariant spectroscopy using deep learning”, Scientific Reports, 2019, Vol. 9, No. 2126, 10 pages, retrieved from the internet: URL:https://www.nature.com/articles/s41598-019-38482-1) - copy provided by applicant.
With respect to claim 19, Sun et al. disclose the method of claim 18, wherein a number of the plurality of wavelengths, associated with the corresponding plurality of spectroscopic intensities (page 7 - the individual spectra of the Ag may be used in the calibration file), a total number of wavelengths with associated spectroscopic intensities output by the spectroscopic instrument during spectroscopic analysis of the sample (page 3 - six replicate Ag spectra were gathered with the spectrometer). Sun et al. fail to disclose the calibration data is less than the total amount of data taken. Chatzidakis et al. disclose the calibration data is less than a total amount of data taken (page 3 - the data [including the wavelengths] may be sectioned into portions, where no portion may encompass the entire [second] dataset). It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Sun et al. to include smaller data subsets, as taught by Chatzidakis et al., in order to allow multiple models to be generated.
Conclusion
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/JURIE YUN/Primary Examiner, Art Unit 2884
September 11, 2026
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