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 because the claims are directed to an abstract idea without significantly more.
Claims 1 and 14 recite “a spectrometer support apparatus” and “spectrometer system” comprising “logic to generate” data, “logic to” perform math, and “logic to output” data; claim 6 recites “a method of determining” comprising “generating” data, “providing… data” to a computer, and “outputting” data.
The specification states the logic is just a programmed device/computer, paragraph [0033], as published, the first logic takes the spectrometer data and plots an intensity graph, paragraph [0036], which is both math, plotting, and a mental step, which can be done with the aid of pen and a paper, the second logic is math, paragraph [0041], and the third logic is insignificant extra-solution activity, see MPEP 2106.05(g).
The broadest reasonable interpretation of the claimed invention is to generate data and perform mathematical analysis on said data. As a result of the broadest reasonable interpretation, these limitations amount to a mental process that could be practically performed in the human mind. Such a process is considered an abstract idea in view of, for example, CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ 2d 1690, 1695 (Fed. Cir. 2011), as the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea.
This judicial exception is not integrated into a practical application because there is no direct application of a judicial exception in a meaningful way. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no positively recited steps are to how the data is measured; instead, the claim only requires acquiring and analyzing data. Without any meaningfully claimed limitation as to how the data is measured, it is not possible for the claimed abstract idea to be integrated into a judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for similar reasons as set forth above as to why the claim is not integrated into a practical application. There does not appear to be any additional limitation in the claim other than the abstract idea of acquiring data and analyzing data. Since there are no additional limitations, the claim does not amount to significantly more than the judicial exception.
While claims 1-5 and 14-20 are interpreted as comprising computer processors performing the method, this is not sufficient to provide significantly more than the abstract idea. A claim can still recite a mental process even if the limitations found in the claim are claimed as being performed on a computer, particularly when the mental process is performed on a generic computer. The courts have held that a mental process that is performed on a generic computer is considered to be an abstract idea as per Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). Additionally, even if the claimed abstract idea was performed on a special purpose computer, it has also been held that using a computer as a tool to perform a mental process is not significantly more than the judicial exception when the steps of the process are recited at a high level of generality and merely use computers as a tool to perform the process. See Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018).
See also Example 47, claim 2, in the July 2024 Subject Matter Eligibility Examples. Available here: https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf
Regarding claims 2-5, 8-13, and 17-20 the claims merely further define the data.
Regarding claims 7 and 16, the claim recites a further abstract step of generating data.
Regarding claim 15, the claim recites further insignificant extra-solution activity.
The Examiner suggests amending the claims to add a step of applying the abstract idea and a non-abstract measuring step to overcome the rejection under 35 USC 101.
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.
(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.
Claims 1-2, 6, 14, 15, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Koujelev, Alexander, and Siu-Lung Lui. "Artificial Neural Networks for Material Identification, Mineralogy and Analytical Geochemistry Based on Laser-Induced Breakdown Spectroscopy." ARTIFICIAL NEURAL NETWORKS ͳ INDUSTRIAL AND CONTROL ENGINEERING APPLICATIONS (2011): 91, hereinafter “Koujelev”.
Regarding claim 1, Koujelev discloses a spectrometer support apparatus (Fig. 1, page 94, Introduction), comprising:
first logic to generate an array of spectrometer output intensities (Fig. 1, Ocean Optics LIBS 2000 spectrometer inherently has logic that generates output intensity) of a sample (as shown in Fig. 1), wherein different ones of the spectrometer output intensities in the array are associated with data representative of different deflection amounts (page 93, Sec. 2. LIBS technique, “emission is collected and delivered to the Ocean Optics LIBS 2000 spectrometer (200 – 970 nm, 0.1 mm resolution) through an optical fibre… The spectra are recorded and analysed with a computer and dedicated software.”);
second logic to provide, to a trained machine-learning computational model (Fig. 9, and 10, page 100, “LIBS-ANN algorithm”), the received array of spectrometer output intensities and at least one ratio between a spectrometer output intensity associated with one deflection amount and a spectrometer output intensity associated with a different deflection amount (pages 103-104, Sec. 3.2, “materials with the same elemental composition but different crystalline structure (or other physical or chemical properties) produce LIBS spectra with different ratios of spectral line intensities.”), wherein the trained machine-learning computational model is to output a concentration of an analyte in the sample (The ANN for quantitative assay requires much higher precision than the sample identification. The output neurons now predict the concentrations, page 106, Sec. 3.3); and
third logic to output the concentration of analyte in the sample (Fig. 1, computer, Fig. 2, concentration, page 94).
Regarding claim 2, Koujelev discloses wherein the spectrometer is an optical spectrometer and the data representative of different deflection amounts is data representative of different wavelengths of radiation (page 93, Sec. 2, LIBS technique, “emission is collected and delivered to the Ocean Optics LIBS 2000 spectrometer”, inherently performs as claimed).
Regarding claim 6, Koujelev discloses a method of determining analyte concentration from spectrometer output intensities (Fig. 1, page 94, Introduction), comprising:
generating an array of spectrometer output intensities (Fig. 1, Ocean Optics LIBS 2000 spectrometer inherently has logic that generates output intensity) of a sample (as shown in Fig. 1), wherein different ones of the spectrometer output intensities in the array are associated with data representative of different deflection amounts (page 93, Sec. 2. LIBS technique, “emission is collected and delivered to the Ocean Optics LIBS 2000 spectrometer (200 – 970 nm, 0.1 mm resolution) through an optical fibre… The spectra are recorded and analysed with a computer and dedicated software.”);
providing, to a trained machine-learning computational model (Fig. 9, and 10, page 100, “LIBS-ANN algorithm”), data representative of at least some of the received array of spectrometer output intensities, wherein the trained machine-learning computational model is to output a concentration of an analyte in the sample (The ANN for quantitative assay requires much higher precision than the sample identification. The output neurons now predict the concentrations, page 106, Sec. 3.3), and the data provided to the trained machine-learning computational model includes at least one ratio between a spectrometer output intensity associated with one deflection amount and a spectrometer output intensity associated with a different deflection amount (pages 103-104, Sec. 3.2, “materials with the same elemental composition but different crystalline structure (or other physical or chemical properties); and
outputting the concentration of analyte in the sample (Fig. 1, computer, Fig. 2, concentration, page 94).
Regarding claim 14, Koujelev discloses a spectrometer system (Fig. 1, page 94, Introduction), comprising:
a spectrometer support module (Fig. 1), including:
first logic to generate an array of spectrometer output intensities (Fig. 1, Ocean Optics LIBS 2000 spectrometer inherently has logic that generates output intensity) of a sample (as shown in Fig. 1), wherein different ones of the spectrometer output intensities in the array are associated with data representative of different deflection amounts (page 93, Sec. 2. LIBS technique, “emission is collected and delivered to the Ocean Optics LIBS 2000 spectrometer (200 – 970 nm, 0.1 mm resolution) through an optical fibre… The spectra are recorded and analysed with a computer and dedicated software.”);
second logic to provide, to a trained machine-learning computational model (Fig. 9, and 10, page 100, “LIBS-ANN algorithm”), at least a ratio of spectrometer output intensities associated with different deflection amounts (pages 103-104, Sec. 3.2, “materials with the same elemental composition but different crystalline structure (or other physical or chemical properties) produce LIBS spectra with different ratios of spectral line intensities.”), wherein the trained machine-learning computational model is to output a concentration of an analyte in the sample (The ANN for quantitative assay requires much higher precision than the sample identification. The output neurons now predict the concentrations, page 106, Sec. 3.3); and
third logic to output the concentration of analyte in the sample (Fig. 1, computer, Fig. 2, concentration, page 94).
Regarding claim 15, Koujelev discloses wherein the third logic is to output the concentration of analyte in the sample to a display device (Fig. 1, computer, Fig. 2, concentration, page 94).
Regarding claim 19, Koujelev discloses wherein the spectrometer is an optical spectrometer and the data representative of different deflection amounts is data representative of different wavelengths of radiation (page 93, Sec. 2, LIBS technique, “emission is collected and delivered to the Ocean Optics LIBS 2000 spectrometer”, inherently performs as claimed).
Claim Rejections - 35 USC § 103
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 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 3-5, 7, 8, 12, 13, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Koujelev, as applied to claims 1 or 6 or 14 and 15, respectively above, and further in view of Al-Haimi et al. (US 2021/0172800 A1), hereinafter “Al-Haimi”.
Regarding claim 3, Koujelev is silent regarding wherein the spectrometer is a mass spectrometer and the data representative of different deflection amounts is data representative of different mass-to-charge ratios.
However, Al-Haimi teaches a spectrometry calibration device (abstract, Fig. 1-3) including wherein the spectrometer is a mass spectrometer and the data representative of different deflection amounts is data representative of different mass-to-charge ratios (claim 17 states that the device can be used in mass spectrometer, the data in paragraphs [0040]-[0062] would inherently be as claimed when using a mass spectrometer).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Koujelev with the teaching of Al-Haimi by including wherein the spectrometer is a mass spectrometer and the data representative of different deflection amounts is data representative of different mass-to-charge ratios as Al-Haimi teaches that these calibration methods can be used with any spectrometer (claim 17, and paragraph [0021], [0033]).
Regarding claim 4, Koujelev is silent regarding wherein the analyte is a single element.
However, Al-Haimi teaches wherein the analyte is a single element (paragraph [0063], Fig 4A).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the device of Koujelev with the teaching of Al-Haimi by including wherein the analyte is a single element as there is nothing to suggest that the device of Koujelev would not perform with a single element, Sec. 3.3.
Regarding claim 5, Koujelev is silent regarding wherein the array of spectrometer output intensities of the sample includes more than two output intensities.
However, Al-Haimi teaches wherein the array of spectrometer output intensities of the sample includes more than two output intensities (paragraph [0069], Figs 4B).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the device of Koujelev with the teaching of Al-Haimi by including wherein the array of spectrometer output intensities of the sample includes more than two output intensities as Al-Haimi teaches that the emission spectra for the samples may produce peaks, amplifications, and/or minor fluctuations at or near specific wavelengths, paragraph [0069].
Regarding claim 7, Koujelev is silent regarding generating, based on the output of the trained machine-learning computational model, a feature relevance indicator associated with one or more of the spectrometer output intensities.
However, Al-Haimi teaches a spectrometry calibration device (abstract, Fig. 1-3) including generating, based on the output of the trained machine-learning computational model, a feature relevance indicator associated with one or more of the spectrometer output intensities (paragraphs [0036], [0061]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Koujelev with the teaching of Al-Haimi by including generating, based on the output of the trained machine-learning computational model, a feature relevance indicator associated with one or more of the spectrometer output intensities in order to identify the respective concentration.
Regarding claim 8, Koujelev is silent regarding wherein the feature relevance indicator associated with a spectrometer output intensity indicates that the spectrometer output intensity was more important to determination of the concentration of analyte in the sample than others of the spectrometer output intensities.
However, Al-Haimi teaches a spectrometry calibration device (abstract, Fig. 1-3) including wherein the feature relevance indicator associated with a spectrometer output intensity indicates that the spectrometer output intensity was more important to determination of the concentration of analyte in the sample than others of the spectrometer output intensities (paragraphs [0036], [0061]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Koujelev with the teaching of Al-Haimi by including wherein the feature relevance indicator associated with a spectrometer output intensity indicates that the spectrometer output intensity was more important to determination of the concentration of analyte in the sample than others of the spectrometer output intensities in order to identify the respective concentration.
Regarding claim 12, Koujelev is silent regarding wherein the feature relevance indicator includes a list of wavelengths more important to determination of the concentration of analyte in the sample than other wavelengths.
However, Al-Haimi teaches a spectrometry calibration device (abstract, Fig. 1-3) including wherein the feature relevance indicator includes a list of wavelengths more important to determination of the concentration of analyte in the sample than other wavelengths (Fig 4B).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Koujelev with the teaching of Al-Haimi by including wherein the feature relevance indicator includes a list of wavelengths more important to determination of the concentration of analyte in the sample than other wavelengths in order to identify the respective concentration with higher accuracy.
Regarding claim 13, Koujelev and Al-Haimi are silent regarding wherein the feature relevance indicator includes a list of mass-to-charge ratios more important to determination of the concentration of analyte in the sample than other mass-to-charge ratios.
However, Al-Haimi teaches this method using a spectrometer, Fig 4B, teaches at claim 17 states that the method can be used in mass spectrometer, and the data in paragraphs [0040]-[0062] would inherently be as claimed when using a mass spectrometer. The Examiner takes Official Notice that mass-to-charge ratios are well-known when using a mass spectrometer.
Regarding claim 16, Koujelev is silent regarding wherein the third logic is to output, to the display device, a feature relevance indicator associated with one or more of the spectrometer output intensities.
However, Al-Haimi teaches a spectrometry calibration device (abstract, Fig. 1-3) including wherein the third logic is to output, to the display device, a feature relevance indicator associated with one or more of the spectrometer output intensities (paragraphs [0036], [0061]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Koujelev with the teaching of Al-Haimi by including wherein the third logic is to output, to the display device, a feature relevance indicator associated with one or more of the spectrometer output intensities in order to identify the respective concentration.
Regarding claim 17, Koujelev is silent regarding wherein the feature relevance indicator associated with a spectrometer output intensity indicates that the spectrometer output intensity was more important to determination of the concentration of analyte in the sample than others of the spectrometer output intensities.
However, Al-Haimi teaches a spectrometry calibration device (abstract, Fig. 1-3) including wherein the feature relevance indicator associated with a spectrometer output intensity indicates that the spectrometer output intensity was more important to determination of the concentration of analyte in the sample than others of the spectrometer output intensities (paragraphs [0036], [0061]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Koujelev with the teaching of Al-Haimi by including wherein the feature relevance indicator associated with a spectrometer output intensity indicates that the spectrometer output intensity was more important to determination of the concentration of analyte in the sample than others of the spectrometer output intensities in order to identify the respective concentration.
Regarding claim 18, Koujelev is silent regarding wherein the spectrometer is a mass spectrometer and the data representative of different deflection amounts is data representative of different mass-to-charge ratios.
However, Al-Haimi teaches a spectrometry calibration device (abstract, Fig. 1-3) including wherein the spectrometer is a mass spectrometer and the data representative of different deflection amounts is data representative of different mass-to-charge ratios (claim 17 states that the device can be used in mass spectrometer, the data in paragraphs [0040]-[0062] would inherently be as claimed when using a mass spectrometer).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Koujelev with the teaching of Al-Haimi by including wherein the spectrometer is a mass spectrometer and the data representative of different deflection amounts is data representative of different mass-to-charge ratios as Al-Haimi teaches that these calibration methods can be used with any spectrometer (claim 17, and paragraph [0021], [0033]).
Regarding claim 20, Koujelev is silent regarding wherein the analyte is a single element.
However, Al-Haimi teaches wherein the analyte is a single element (paragraph [0063], Fig 4A).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the device of Koujelev with the teaching of Al-Haimi by including wherein the analyte is a single element as there is nothing to suggest that the device of Koujelev would not perform with a single element, Sec. 3.3.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Koujelev and Al-Haimi, as applied to claims 6 and 7 above, and further in view of Velásquez, Marizú, et al. "Improved elemental quantification in copper ores by laser-induced breakdown spectroscopy with judicious data processing." Spectrochimica Acta Part B: Atomic Spectroscopy 188 (2021): 106343, hereinafter “Velásquez”.
Regarding claim 9, Koujelev is silent regarding wherein the feature relevance indicator associated with two spectrometer output intensities indicates that a combination of the two spectrometer output intensities was more important to determination of the concentration of analyte in the sample than other combinations of spectrometer output intensities.
However, Velásquez teaches a spectrometry data processing device (abstract) including wherein the feature relevance indicator associated with two spectrometer output intensities indicates that a combination of the two spectrometer output intensities was more important to determination of the concentration of analyte in the sample than other combinations of spectrometer output intensities (Sec. 2.3.2, “Internal standard for spectral normalization” pages 188-189).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Koujeleve with the teaching of Velásquez by including wherein the feature relevance indicator associated with two spectrometer output intensities indicates that a combination of the two spectrometer output intensities was more important to determination of the concentration of analyte in the sample than other combinations of spectrometer output intensities in order to implement an analysis of feature importance from the outputs to determine the sample information.
Regarding claim 10, Koujelev is silent regarding wherein the feature relevance indicator associated with the two spectrometer output intensities indicates that a ratio of the two spectrometer output intensities was more important to determination of the concentration of analyte in the sample than other ratios of spectrometer output intensities.
However, Velásquez teaches a spectrometry data processing device (abstract) including wherein the feature relevance indicator associated with the two spectrometer output intensities indicates that a ratio of the two spectrometer output intensities was more important to determination of the concentration of analyte in the sample than other ratios of spectrometer output intensities (Sec. 2.3.2, “Internal standard for spectral normalization” pages 188-189).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Koujeleve with the teaching of Velásquez by including wherein the feature relevance indicator associated with the two spectrometer output intensities indicates that a ratio of the two spectrometer output intensities was more important to determination of the concentration of analyte in the sample than other ratios of spectrometer output intensities in order to implement an analysis of feature importance from the outputs to determine the sample information.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Koujelev and Al-Haimi, as applied to claims 6 and 7 above, and further in view of Jull, Harrisson, et al. "Laser-induced breakdown spectroscopy analysis of sodium in pelletised pasture samples." 2015 6th international conference on automation, robotics and applications (ICARA). IEEE, 2015, hereinafter “Jull”.
Regarding claim 11, Koujelev is silent regarding wherein generating the feature relevance indicator includes performing linear regression feature relevance, logistic regression feature relevance, or decision tree feature relevance.
However, Jull teaches a spectrometry data processing device (abstract) including wherein generating the feature relevance indicator includes performing linear regression feature relevance, logistic regression feature relevance, or decision tree feature relevance (Sec. II. D. “Plartial Least Squares Analysis”, page 266).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Koujeleve with the teaching of Jull by including wherein generating the feature relevance indicator includes performing linear regression feature relevance, logistic regression feature relevance, or decision tree feature relevance in order to analyze the relevance of peaks and peak ratios, giving better sample analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Boskamp (US 2019/0096652) teaches a method of measuring a sample using a spectrometer, by normalizing an intensity profile, similar to the claimed invention.
Pfall (US 2020/0176238) teaches a method of measuring using a spectrometer, using extrapolation of peaks to determine sample information, similar to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOMINIC J BOLOGNA whose telephone number is (571)272-9282. The examiner can normally be reached Monday - Friday 7:30am-3:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kara E Geisel can be reached at (571) 272-2416. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DOMINIC J BOLOGNA/ Primary Examiner, Art Unit 2877