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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/09/2025 has been entered.
Response to Arguments
Applicant's arguments filed 09/09/2025 with respect to the rejection(s) of claims 18, 36, and 38 under 35 U.S.C. 101 have been fully considered and are persuasive. The abstract ideas are clearly used by the gas chromatograph to ascertain the composition of the substance sample. This amounts to applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b).
Applicant’s arguments, see Remarks, filed 09/09/2025, with respect to the rejection(s) of claims 18, 36, and 38 under 35 U.S.C. 103 have been fully considered and are persuasive. Kanazawa (US 20230280317 A1) is does not teach that steps a-d occur within a first measurement cycle as the process in Kanazawa is different from that of the instant application. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Joshi (US 20230160863 A1), Kudo (US 20220074902 A1), and Kawamura (US 11841373 B2). Kanazawa is still used as a secondary reference to teach claim 22 as they do show using historical data from chromatographs of the same or similar construction to train an artificial intelligence and it would be obvious to combine that feature of Kanazawa with the combination of Joshi (US 20230160863 A1), Kudo (US 20220074902 A1), and Kawamura (US 11841373 B2) has highlighted in the rejection below.
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.
Claim 39 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because a computer product is directed to a computer program per se. Para. [0023] of the PG-Pub states that the computer program product can be fully formed as software.
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 18-24, 28-30 and 36-39 are rejected under 35 U.S.C. 103 as being unpatentable over Joshi (US 20230160863 A1) as modified by Kudo (US 20220074902 A1), and Kawamura (US 11841373 B2).
With respect to claim 18,
Joshi teaches,
a) quantitatively ascertaining by a detector, a concentration of a first component of the substance sample and outputting the concentration to an artificial intelligence as a first partial measured value; (Para. [0005] teaches “The method includes collecting a sample from the column and introducing the sample to a two-dimensional gas chromatography system to determine a time-series concentration of one or more components of the sample. The method includes predicting quantitative isotherm information of the at least one component, based on the isotherm of the at least one other component.” Para. [0106] teaches “determining, via a machine learning model, using a QSAR attributes of feed components along with extracted isotherm data”)
b) ascertaining a concentration of a second and a third component of a divided substance sample via the artificial intelligence utilizing the first partial measured value and outputting the ascertained concentrations; (Para. [0005] teaches “The method includes predicting quantitative isotherm information of the at least one component, based on the isotherm of the at least one other component.” Para. [0016] teaches “graphs illustrating (1) an integrated approach used to model a system containing hundreds of components from multi-component pulse experiments and/or (2) track the competitive behavior of each component in a multi-component system when the concentration of all the components are continuously varying (as in an adsorption process separating a multicomponent feed), according to an embodiment.” Para. [0078] teaches “method to construct a multi-component competitive isotherm model using machine-learning,”)
c) quantitatively ascertaining, by the detector, the concentration of the second component of the divided substance sample and outputting the quantitatively ascertained concentration to the artificial intelligence as a second partial measured value; (Para. [0070] teaches “The computed isotherm(s) may be used either alone or as part of an adsorption process model to dynamically simulate the separation profile of every component in a multicomponent mixture, using the given adsorbent and solvent.” (i.e. all components are found so the second component is ascertained.))
and d) ascertaining the concentration of the third component of the divided substance sample via the artificial intelligence utilizing the first and second partial measured values and outputting the ascertained concentration; (Para. [0105] teaches “integrating the slope of adsorption isotherm with time-series concentration of at least one of the one or more components to determine an adsorption isotherm of the at least one component;” Para. [0106] teaches “determining, via a machine learning model, using a QSAR attributes of feed components along with extracted isotherm data” Para. [0110] teaches “predicting, via a machine learning model, the amount of new component adsorbed for a liquid concentration at equilibrium.” (i.e. Where the new component would represent the third component.))
wherein steps a) to d) are performed within a first measurement cycle of a gas chromatograph in which the substance sample is analyzed to ascertain the composition of the substance sample via the gas chromatograph. (Fig. 14 shows a cycle that contains all steps for determining the composition of the sample. Therefore, all steps occur during a measurement cycle.)
Joshi does not explicitly teach,
the substance sample formed as the mixture of gases; artificial intelligence stored in the memory of the evaluator.
Kudo teaches,
the substance sample formed as the mixture of gases. (Para(s). [0052-0053] teach “The gas chromatograph 10 separates a sample or the like into components based on physical properties or chemical properties. A sample or the like is gas or gaseous when being introduced into the separation column 14 and is referred to as a sample gas. The carrier gas flow path 11 is a flow path for a carrier gas such as helium and introduces the carrier gas into the sample introducer 12 (arrow A1). The sample introducer 12 includes a chamber such as a sample vaporization chamber into which a sample or the like is introduced, temporarily contains the sample or the like injected by an injector (not shown) such as a syringe or an autosampler, vaporizes the sample or the like in a case where the sample or the like is liquid and introduces a sample gas into the separation column 14 (arrow A2).” (i.e. a liquid sample is vaporized and then analyzed by a gas chromatograph.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Joshi wherein the substance sample formed as the mixture of gases such as that of Kudo.
One of ordinary skill would have been motivated to modify Joshi, because a gas chromatograph analyzes a gas sample. If the sample begins as a liquid sample such as the sample in Joshi it must be vaporized before it analyzed as seen in Kudo. Therefore, the substance sample must be formed as a mixture of gases.
The combination of Joshi and Kudo does not explicitly teach,
artificial intelligence stored in the memory of the evaluator.
Kawamura teaches,
artificial intelligence stored in the memory of the evaluator. (Col 3. Ln(s). [23-28] “the above embodiments can also be implemented by supplying a program for implementing one or more functions of the above embodiments to a system or an apparatus via a network or a storage medium and by reading and executing the program with one or more processors of the computer of the system or the apparatus.” Col. 6 Ln(s). [54-58] teach “The learning-model acquisition unit 43 acquires the learning model generated by the learning-model generation unit 42. In the case where the learning model is stored in the database 22, the learning-model acquisition unit 43 obtains the learning model from the database.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Joshi and Kudo with artificial intelligence stored in the memory of the evaluator such as that of Kawamura.
One of ordinary skill would have been motivated to modify the combination of Joshi and Kudo, because as seen in Joshi Para(s). [0108-0109] a processor is used to generate and run a machine learning algorithm and storing the artificial intelligence as seem in Kawamura would be necessary for it to function on a computational device.
With respect to claim 19,
Joshi further teaches,
The method for ascertaining the composition of the substance sample as claimed in claim 18, wherein the artificial intelligence comprises at least one of a neural network, fuzzy logic and a statistics module. (Para. [0117] teaches “wherein obtaining comprises selecting via a processor that includes (a) a selection formulation such as step-wise regression, elastic-net, LASSO applied to (b) a predictor that could be any or a combination of linear models, nonlinear models, ensemble models (such as random forests), black-box models (such as neural networks), or a QSAR feature set that is maximally predictive of the equilibrium partition of the components between the adsorbed and bulk phase.”)
With respect to claim 20,
Joshi further teaches,
The method for ascertaining the composition of the substance sample as claimed in claim 18, wherein the artificial intelligence is trained by a training dataset. (Para. [0107] teaches “training a machine learning algorithm to identify isotherm QSAR attributes of potential components of the multi-component feed;”)
With respect to claim 21,
Joshi further teaches,
The method for ascertaining the composition of the substance sample as claimed in claim 19, wherein the artificial intelligence is trained by a training dataset. (Para. [0107] teaches “training a machine learning algorithm to identify isotherm QSAR attributes of potential components of the multi-component feed;”)
With respect to claim 23,
Joshi further teaches,
The method for ascertaining the composition of the substance sample as claimed in claim 18, wherein, during step e), the concentration of the third component of the substance sample is ascertained quantitatively and the concentration of the third component is output as a third partial measured value. (Para. [0029] teaches “By contacting the hydrocarbon sample with the first and second substrates, the hydrocarbon sample can be separated into three fractions, one containing saturated hydrocarbons, one containing single ring aromatic hydrocarbons, and one containing unsaturated, multi-ring aromatic hydrocarbons.”)
With respect to claim 24,
Joshi further teaches,
The method for ascertaining the composition of the substance sample as claimed in claim 18, wherein partial measured values and corresponding concentrations of components of the substance sample ascertained by the artificial intelligence are provided for machine learning of the artificial intelligence. (Para. [0109] teaches “generating the machine learning model based on the machine learning algorithm coefficients”)
With respect to claim 28,
Joshi further teaches,
The method for ascertaining the composition of the substance sample as claimed in claim 18, wherein at least one of step b) and d) is performed while taking into account a process parameter. (Para. [0002] teaches “Refinery process conditions (such as temperature, pressure, and/or type of catalyst) are dependent on the chemical composition of a refinery feed.”)
With respect to claim 29,
Joshi further teaches,
The method for ascertaining the composition of the substance sample as claimed in claim 28, wherein the process parameter comprises at least one of a temperature, a pressure, a pH value and an electrical quantity. (Para. [0002] teaches “Refinery process conditions (such as temperature, pressure, and/or type of catalyst) are dependent on the chemical composition of a refinery feed.” Para. [0025] teaches “The adsorbent column(s) 202 that can be used may be placed in an oven 204 operated at the desired temperature, for example 150° C. A preheat coil 206 within the oven can be used to ensure that the solvent/sample is at a desired operating temperature before entering the adsorbent bed (of the column(s) 202) at multiple flow rates.”)
With respect to claim 30,
Joshi further teaches,
The method for ascertaining the composition of the substance sample as claimed in claim 18, wherein an auxiliary measured value of an auxiliary analysis apparatus is provided during the first measurement cycle for the concentration of at least one component of the substance sample. (Para. [0029] teaches “In some embodiments, one column can contain a substrate that exhibits preferential affinity for polar compounds such as unsaturated, multi-ring aromatic hydrocarbons, while a second column can contain a substrate that exhibits preferential affinity for other polar compounds such as single ring aromatic hydrocarbons.” Para. [0038] teaches “Mass spectrometry or a flame ionization detector (FID) can be used for the signal detection or combination of thereof”)
With respect to claim 36,
Joshi teaches,
a processor; (Para. [0108] teaches “via a processor,”)
Wherein the evaluator is configured to: a) quantitatively a concentration of a first component of a divided substance sample and outputting the concentration to an artificial intelligence as a first partial measured value; (Para. [0005] teaches “The method includes collecting a sample from the column and introducing the sample to a two-dimensional gas chromatography system to determine a time-series concentration of one or more components of the sample. The method includes predicting quantitative isotherm information of the at least one component, based on the isotherm of the at least one other component.” Para. [0106] teaches “determining, via a machine learning model, using a QSAR attributes of feed components along with extracted isotherm data”)
b) ascertain a concentration of a second and a third component of a divided substance sample via the artificial intelligence utilizing the first partial measured value and output the ascertained concentrations; (Para. [0005] teaches “The method includes predicting quantitative isotherm information of the at least one component, based on the isotherm of the at least one other component.” Para. [0016] teaches “graphs illustrating (1) an integrated approach used to model a system containing hundreds of components from multi-component pulse experiments and/or (2) track the competitive behavior of each component in a multi-component system when the concentration of all the components are continuously varying (as in an adsorption process separating a multicomponent feed), according to an embodiment.” Para. [0078] teaches “method to construct a multi-component competitive isotherm model using machine-learning,”)
c) quantitatively ascertaining, by the detector, the concentration of the second component of the divided substance sample and outputting the quantitatively ascertained concentration to the artificial intelligence as a second partial measured value; (Para. [0070] teaches “The computed isotherm(s) may be used either alone or as part of an adsorption process model to dynamically simulate the separation profile of every component in a multicomponent mixture, using the given adsorbent and solvent.” (i.e. all components are found so the second component is ascertained.)
and d) ascertain the concentration of the third component of the divided substance sample via the artificial intelligence utilizing the first and second partial measured values and outputting the ascertained concentration; (Para. [0105] teaches “integrating the slope of adsorption isotherm with time-series concentration of at least one of the one or more components to determine an adsorption isotherm of the at least one component;” Para. [0106] teaches “determining, via a machine learning model, using a QSAR attributes of feed components along with extracted isotherm data” Para. [0110] teaches “predicting, via a machine learning model, the amount of new component adsorbed for a liquid concentration at equilibrium.” (i.e. Where the new component would represent the third component.))
wherein steps a) to d) are performed within a first measurement cycle of a gas chromatograph in which the substance sample is analyzed to ascertain the composition of the substance sample via the gas chromatograph. (Fig. 14 shows a cycle that contains all steps for determining the composition of the sample. Therefore, all steps occur during a measurement cycle.)
Joshi does not explicitly teach,
Memory; the substance sample formed as the mixture of gases; artificial intelligence stored in the memory of the evaluator.
Kudo teaches,
the substance sample formed as the mixture of gases. (Para(s). [0052-0053] teach “The gas chromatograph 10 separates a sample or the like into components based on physical properties or chemical properties. A sample or the like is gas or gaseous when being introduced into the separation column 14 and is referred to as a sample gas. The carrier gas flow path 11 is a flow path for a carrier gas such as helium and introduces the carrier gas into the sample introducer 12 (arrow A1). The sample introducer 12 includes a chamber such as a sample vaporization chamber into which a sample or the like is introduced, temporarily contains the sample or the like injected by an injector (not shown) such as a syringe or an autosampler, vaporizes the sample or the like in a case where the sample or the like is liquid and introduces a sample gas into the separation column 14 (arrow A2).” (i.e. a liquid sample is vaporized and then analyzed by a gas chromatograph.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Joshi wherein the substance sample formed as the mixture of gases such as that of Kudo.
One of ordinary skill would have been motivated to modify Joshi, because a gas chromatograph analyzes a gas sample. If the sample begins as a liquid sample such as the sample in Joshi it must be vaporized before it analyzed as seen in Kudo. Therefore, the substance sample must be formed as a mixture of gases.
The combination of Joshi and Kudo does not explicitly teach,
Memory; artificial intelligence stored in the memory of the evaluator.
Kawamura teaches,
memory; artificial intelligence stored in the memory of the evaluator. (Col 3. Ln(s). [23-28] “the above embodiments can also be implemented by supplying a program for implementing one or more functions of the above embodiments to a system or an apparatus via a network or a storage medium and by reading and executing the program with one or more processors of the computer of the system or the apparatus.” Col. 6 Ln(s). [54-58] teach “The learning-model acquisition unit 43 acquires the learning model generated by the learning-model generation unit 42. In the case where the learning model is stored in the database 22, the learning-model acquisition unit 43 obtains the learning model from the database.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Joshi and Kudo with memory; artificial intelligence stored in the memory of the evaluator such as that of Kawamura.
One of ordinary skill would have been motivated to modify the combination of Joshi and Kudo, because as seen in Joshi Para(s). [0108-0109] a processor is used to generate and run a machine learning algorithm and storing the artificial intelligence as seem in Kawamura would be necessary for it to function on a computational device.
With respect to claim 37,
Joshi further teaches,
A gas chromatograph for ascertaining a composition of a substance sample, the gas chromatograph comprising: at least one separating apparatus; and one detector connected to the evaluation unit as claimed in claim 36. (Abstract teaches “The method includes collecting a sample from the column and introducing the sample to a two-dimensional gas chromatograph to determine a time-series concentration of one or more components of the sample.” Para. [0120] teaches “Fraction of the column effluent were collected at time intervals from 0.25 to 1.0 minutes and analyzed by gas chromatography (GC) using a Agilent 30 m DB-5 column temperature programmed from 50-250° C. and a flame ionization detector.”)
With respect to claim 38,
Joshi teaches,
a) quantitatively ascertaining, by a detector, a concentration of a first component of a divided substance sample and outputting the concentration to an artificial intelligence as a first partial measured value; (Para. [0005] teaches “The method includes collecting a sample from the column and introducing the sample to a two-dimensional gas chromatography system to determine a time-series concentration of one or more components of the sample. The method includes predicting quantitative isotherm information of the at least one component, based on the isotherm of the at least one other component.” Para. [0106] teaches “determining, via a machine learning model, using a QSAR attributes of feed components along with extracted isotherm data”)
b) ascertaining a concentration of a second and a third component of a divided substance sample via the artificial intelligence utilizing the first partial measured value and output the ascertained concentrations; (Para. [0005] teaches “The method includes predicting quantitative isotherm information of the at least one component, based on the isotherm of the at least one other component.” Para. [0016] teaches “graphs illustrating (1) an integrated approach used to model a system containing hundreds of components from multi-component pulse experiments and/or (2) track the competitive behavior of each component in a multi-component system when the concentration of all the components are continuously varying (as in an adsorption process separating a multicomponent feed), according to an embodiment.” Para. [0078] teaches “method to construct a multi-component competitive isotherm model using machine-learning,”)
c) quantitatively ascertaining, by the detector, the concentration of the second component of the divided substance sample and outputting the quantitatively ascertained concentration to the artificial intelligence as a second partial measured value; (Para. [0070] teaches “The computed isotherm(s) may be used either alone or as part of an adsorption process model to dynamically simulate the separation profile of every component in a multicomponent mixture, using the given adsorbent and solvent.”)
and d) ascertaining the concentration of the third component of the divided substance sample via the artificial intelligence utilizing the first and second partial measured values and outputting the ascertained concentration; (Para. [0105] teaches “integrating the slope of adsorption isotherm with time-series concentration of at least one of the one or more components to determine an adsorption isotherm of the at least one component;” Para. [0106] teaches “determining, via a machine learning model, using a QSAR attributes of feed components along with extracted isotherm data” Para. [0110] teaches “predicting, via a machine learning model, the amount of new component adsorbed for a liquid concentration at equilibrium.” Where the new component would represent the third component.)
wherein steps a) to d) are performed within a first measurement cycle of a gas chromatograph in which the substance sample is analyzed to ascertain the composition of the substance sample via the gas chromatograph. (Fig. 14 shows a cycle that contains all steps for determining the composition of the sample. Therefore, all steps occur during a measurement cycle.)
Joshi does not explicitly teach,
an evaluator including a processor, memory and an executable artificial intelligence stored in the memory;
an auxiliary analysis apparatus formed as a Raman spectrometer or continuous gas analyzer and connected to the evaluator,
a divided substance sample formed as a mixture of gases.
Kudo teaches,
the substance sample formed as the mixture of gases. (Para(s). [0052-0053] teach “The gas chromatograph 10 separates a sample or the like into components based on physical properties or chemical properties. A sample or the like is gas or gaseous when being introduced into the separation column 14 and is referred to as a sample gas. The carrier gas flow path 11 is a flow path for a carrier gas such as helium and introduces the carrier gas into the sample introducer 12 (arrow A1). The sample introducer 12 includes a chamber such as a sample vaporization chamber into which a sample or the like is introduced, temporarily contains the sample or the like injected by an injector (not shown) such as a syringe or an autosampler, vaporizes the sample or the like in a case where the sample or the like is liquid and introduces a sample gas into the separation column 14 (arrow A2).” (i.e. a liquid sample is vaporized and then analyzed by a gas chromatograph.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Joshi wherein the substance sample formed as the mixture of gases such as that of Kudo.
One of ordinary skill would have been motivated to modify Joshi, because a gas chromatograph analyzes a gas sample. If the sample begins as a liquid sample such as the sample in Joshi it must be vaporized before it analyzed as seen in Kudo. Therefore, the substance sample must be formed as a mixture of gases.
The combination of Joshi and Kudo does not explicitly teach,
an evaluator including a processor, memory and an executable artificial intelligence stored in the memory;
an auxiliary analysis apparatus formed as a Raman spectrometer or continuous gas analyzer and connected to the evaluator.
Kawamura teaches,
an evaluator including a processor, memory and an executable artificial intelligence stored in the memory; (Col 3. Ln(s). [23-28] “the above embodiments can also be implemented by supplying a program for implementing one or more functions of the above embodiments to a system or an apparatus via a network or a storage medium and by reading and executing the program with one or more processors of the computer of the system or the apparatus.” Col. 6 Ln(s). [54-58] teach “The learning-model acquisition unit 43 acquires the learning model generated by the learning-model generation unit 42. In the case where the learning model is stored in the database 22, the learning-model acquisition unit 43 obtains the learning model from the database.”)
an auxiliary analysis apparatus formed as a Raman spectrometer or continuous gas analyzer and connected to the evaluator, (Col 5. Ln(s). [5-14] teach “Examples of the physical analytical method include photoelectric spectroscopy, infrared absorption spectroscopy, nuclear magnetic resonance spectroscopy, fluorescent spectroscopy, fluorescent X-ray spectroscopy, visible-ultraviolet absorption spectroscopy, Raman spectroscopy, an atomic absorption method, frame emission spectroscopy, emission spectroscopy, X-ray absorption spectroscopy, an X-ray diffraction method, electron spin resonance spectroscopy using paramagnetic resonance absorption or the like, and a thermo-analytical method.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Joshi and Kudo with an evaluator including a processor, memory and an executable artificial intelligence stored in the memory and an auxiliary analysis apparatus formed as a Raman spectrometer or continuous gas analyzer and connected to the evaluator such as that of Kawamura.
One of ordinary skill would have been motivated to modify the combination of Joshi and Kudo, because storing the artificial intelligence would be necessary for it to function on a computational device and according to The University of Cambridge (2016) Raman Spectroscopy is highly versatile, suitable for various sample types including solids, liquids, and gases, and requires minimal sample preparation.
With respect to claim 39,
Joshi further teaches,
A computer program product for ascertaining a composition of a substance sample comprising an artificial intelligence and a variable training dataset stored in the memory, wherein the computer program product is configured to perform the method as claimed in claim 18. (Para. [0107] teaches “training a machine learning algorithm to identify isotherm QSAR attributes of potential components of the multi-component feed;” Para. [0110] teaches “predicting, via a machine learning model, the amount of new component adsorbed for a liquid concentration at equilibrium.”)
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Joshi (US 20230160863 A1) as modified by Kudo (US 20220074902 A1) and Kawamura (US 11841373 B2) as applied to claim 20 above, and further in view of Kanazawa (US 20230280317 A1).
With respect to claim 22,
The combination of Joshi and Kanazawa teaches the method for ascertaining the composition of the substance sample as claimed in claim 20.
Joshi does not explicitly teach,
wherein the training dataset comprises data on historical ascertainments of compositions of substance samples on gas chromatographs of the same or similar construction.
Kanazawa teaches,
wherein the training dataset comprises data on historical ascertainments of compositions of substance samples on gas chromatographs of the same or similar construction. (Para. [0002] teaches “Since the area value or height value of the peak observed in the chromatogram corresponds to the content or concentration of the component corresponding to the peak, the component can be quantitatively determined from the area value or height value of the peak.” Para. [0006] teaches “In the peak detection method using machine learning, a discriminator for peak detection is generated in advance by performing learning using a lot of chromatogram waveforms and correct answer information including feature values such as accurate positions, area values, and height values of peaks observed in the chromatogram waveforms as teacher data (also referred to as training data or learning data).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Joshi, Kudo, and Kawamura wherein the training dataset comprises data on historical ascertainments of compositions of substance samples on gas chromatographs of the same or similar construction such as that of Kanazawa.
One of ordinary skill would have been motivated to modify Joshi, because if the training data was not gathered on the same or similar chromatographs it would not be applicable to the current experiment and the results would be less accurate.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Joshi (US 20230160863 A1) as modified by Kudo (US 20220074902 A1) and Kawamura (US 11841373 B2) as applied to claim 18 above, and further in view of Kudo (2021) (WO 2021095144 A1).
With respect to claim 25,
the combination of Joshi, Kudo, and Kawamura does not explicitly teach,
The method for ascertaining the composition of the substance sample as claimed in claim 18, wherein, during step f), a concentration of a residual component of the substance sample is detected and a warning is issued if the concentration of the residual component exceeds an adjustable threshold value.
Kudo (2021) teaches,
The method for ascertaining the composition of the substance sample as claimed in claim 18, wherein, during step f), a concentration of a residual component of the substance sample is detected and a warning is issued if the concentration of the residual component exceeds an adjustable threshold value. (Para. [0051] teaches “The notification may, for example, be a notification that the calculated concentration of the analyte in the sample is higher than a specified concentration if the peak intensity or peak area exceeds a threshold value.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Joshi, Kudo, and Kawamura wherein, during step f), a concentration of a residual component of the substance sample is detected and a warning is issued if the concentration of the residual component exceeds an adjustable threshold value such as that of Kudo (2021).
One of ordinary skill would have been motivated to modify the combination of Joshi, Kudo, and Kawamura, because according to Para. [0051] of Kudo (2021) “It is also possible to notify that the sample contains an influencing substance (second substance) that affects the analysis of the target substance (first substance).” Therefore, to avoid unwanted influences of the analysis one would be motivated to modify the combination of Joshi and Kanazawa.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Joshi (US 20230160863 A1) as modified by Kudo (US 20220074902 A1) and Kawamura (US 11841373 B2) as applied to claim 18 above, and further in view of Yamada (US 20220196615 A1).
With respect to claim 26,
the combination of Joshi, Kudo, and Kawamura does not explicitly teach,
The method for ascertaining the composition of the substance sample as claimed in claim 18, wherein the concentration of at least one of the second and third components of the substance sample ascertained by the artificial intelligence are output with at least one of an error margin and a confidence interval.
Yamada teaches,
wherein the concentration of at least one of the second and third components of the substance sample ascertained by the artificial intelligence are output with at least one of an error margin and a confidence interval. (Para. [0089] teaches “Specifically, in the foregoing embodiment, the deep learning is used as a method for detecting the peaks. Alternatively, other methods of machine learning may be used, or still alternatively, a method other than machine learning may be used. For example, as the method other than machine learning, a symmetry factor based on evaluation of the left-to-right symmetry of the peak may be provided as the confidence information for the peak.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Joshi, Kudo, and Kawamura wherein the concentration of at least one of the second and third components of the substance sample ascertained by the artificial intelligence are output with at least one of an error margin and a confidence interval such as that of Kudo.
One of ordinary skill would have been motivated to modify the combination of Joshi, Kudo, and Kawamura, because according to Para. [0089] of Yamada “What is important is to acquire, in the process of detecting the peak, the information indicating reliability of detecting the peak.”
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Joshi (US 20230160863 A1) as modified by Kudo (US 20220074902 A1) and Kawamura (US 11841373 B2) as applied to claim 18 above, and further in view of Freeman (US 20230368914 A1).
With respect to claim 27,
the combination of Joshi, Kudo, and Kawamura does not explicitly teach,
wherein a difference is ascertained between a concentration of the at least three components ascertained by the artificial intelligence and the corresponding partial measured value and a warning is issued if the difference exceeds an adjustable anomaly threshold value in terms of amount.
Freeman teaches,
wherein a difference is ascertained between a concentration of the at least three components ascertained by the artificial intelligence and the corresponding partial measured value and a warning is issued if the difference exceeds an adjustable anomaly threshold value in terms of amount. (Para. [0091] teaches “Having predicted the state, an output signal may be generated, such as a warning signal or a control signal, which may be indicative of the predicted state deviating, or conversely not deviating, from a reference state.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Joshi, Kudo, and Kawamura wherein the concentration of at least one of the second and third components of the substance sample ascertained by the artificial intelligence are output with at least one of an error margin and a confidence interval such as that of Kudo.
One of ordinary skill would have been motivated to the combination of Joshi, Kudo, and Kawamura, because if the prediction from the AI deviates from the actual measured values, then the AI algorithm is not making accurate predictions and it needs to be addressed before further analysis to ensure accurate results.
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Joshi (US 20230160863 A1) as modified by Kudo (US 20220074902 A1) and Kawamura (US 11841373 B2) as applied to claim 28 above, and further in view of Fasanotti (US 20230417713 A1).
With respect to claim 31,
the combination of Joshi, Kudo, and Kawamura does not explicitly teach,
The method for ascertaining the composition of the substance sample as claimed in claim 28, wherein the auxiliary analysis apparatus has a measurement cycle duration which is less than a measurement cycle duration of the first measurement cycle.
Fasanotti teaches,
wherein the auxiliary analysis apparatus has a measurement cycle duration which is less than a measurement cycle duration of the first measurement cycle. (Para. [0085] teaches “Conveniently, module 3 for mass spectrometry comprises a mass spectrometer to measure the mass of the compounds present within the sample S to be analyzed, in particular after said compounds have come out, separately between them, from the chromatographic module 2.” Para. [0010] teaches “Reduced analysis times and limited sample volumes are also advantageous features of electrophoretic techniques”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Joshi, Kudo, and Kawamura wherein the auxiliary analysis apparatus has a measurement cycle duration which is less than a measurement cycle duration of the first measurement cycle such as that of Fasanotti.
One of ordinary skill would have been motivated to modify the combination of Joshi, Kudo, and Kawamura, because the mass spectrometer would reduce total analysis times as seen in Para. [0010] of Fasanotti.
Conclusion
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/JOSHUA L FORRISTALL/Examiner, Art Unit 2863
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857