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 .
Response to Amendment
Applicant’s amendments to the claims, filed 03/12/2026, are accepted and appreciated by the
Examiner.
Response to Arguments
Applicant's arguments, see Remarks, filed 03/12/2026, with respect to the rejection(s) of claims 1 and 8 under 35 U.S.C. 101 have been fully considered but they are not persuasive. With respect to the arguments regarding 35 U.S.C. 101 the present claims do include an abstract idea. Extracting a peak using a first and second method is mathematical concept as seen in Para. [0020] of the specification where it lists that the methods for extracting a peak are a link point method, a horizontal method, a new baseline method, and a minimum chromatogram point method, all of which are well-known mathematical algorithms for determining a peak. Determining absence of a peak by comparing peak information to standard data (i.e. a comparison) and creating learning data (i.e. categorizing information) are mental processes as they can be done in the human mind using observation judgement and opinion. Therefore, the claims do cover an abstract idea.
As seen in MPEP 2106.05(a) “After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016).” From the claim It is unclear how the use of the first and second methods reduce the possibility of loss or creates a high-accuracy discriminator. Therefore, it is unclear from the claim as written if it reflects the improvement as argued. Amending measurement data to chromatogram data does not integrate the abstract idea into a practical application as it just ties the data to a particular technological environment or field of use – see MPEP 2106.05(h). The claim does not measure anything with a chromatogram and just acquires its data.
Defining a baseline and extracting a peak are abstract ideas as they amount to mental processes and mathematical concepts. Defining a baseline can be done using judgment based off observed data and extracting a peak is done using an algorithm as seen in para. [0020] of the specification.
Applicant’s arguments, see Remarks, filed 03/12/2026, with respect to the rejection(s) of claims 1 and 8 under 35 U.S.C. 103 have been fully considered and are persuasive. The combination of Noda does not explicitly teach “wherein the first method and the second method define a baseline and extract a peak from a waveform of the chromatogram data.” Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Noda (US 20170336370 A1), Bhardwaj (US 20230086542 A1), and Noda (2019) (US 20190011408 A1). However, Noda (US 20170336370 A1) teaches comparing a number models to initial values of their respective model where the initial value of each model is viewed as a standard value as they are found using an existing technique. (Para. [0077]) Para. [0100] shows adding model peaks to the initial model value to determine the presence or absence of peaks. Collating is defined as gathering or assembling items and therefore this addition as it is assembling multiple peaks together is viewed as collating. Therefore, Noda teaches “determining absence of a peak by collating the peak information extracted using the first method with peak information which is prepared in advance of a standard sample which has a same attribute as each of the plurality of reference samples.”
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
With respect to claim 1,
Step 2A Prong 1:
the following bold limitations are considered abstract:
“acquiring chromatogram data including a plurality of peaks in chromatogram intensity with respect to a predetermined parameter for each of a plurality of reference samples having known attributes;
extracting peak information related to the plurality of peaks using a first method prepared in advance for the chromatogram data of each of the plurality of reference samples;
determining absence of a peak by collating the peak information extracted using the first method with peak information which is prepared in advance of a standard sample which has a same attribute as each of the plurality of reference samples;
extracting peak information related to the plurality of peaks using a second method which differs from the first method in an algorithm or/and a parameter for extracting a peak for the chromatogram data of the reference samples, the chromatogram data being determined to have absence of a peak;
determining absence of a peak by collating the peak information extracted using the second method with the peak information of the standard sample;
and acquiring a feature amount corresponding to each of the plurality of peaks from the chromatogram data that is extracted using the first method or the second method, and is determined to have no absence of a peak, to create learning data,
wherein the first method and the second method define a baseline and extract a peak from a waveform of the chromatogram data.”
The above bolded limitations are directed to abstract ideas and would fall within the “Mathematical Concept” and “Mental Process” groupings of abstract ideas. The first method and second methods for extracting peaks are mathematical concepts as seen in Para. [0020] of the specification where it lists that the methods can be a link point method, a horizontal method, a new baseline method, and a minimum chromatogram point method. All of which are well-known mathematical algorithms for determining a peak. According to MPEP 2106.04(C) “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Determining absence of peaks and creating learning data from the found peak information can be done in the human mind using observation, judgement, and opinion. Likewise, defining a baseline can be done in the human mind by observing data and making judgments about said data.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements –
“acquiring chromatogram data including a plurality of peaks in chromatogram intensity with respect to a predetermined parameter for each of a plurality of reference samples having known attributes;”
Examiner views these limitations amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
As such Examiner does NOT view that the claims
-Improve the functioning of a computer, or to any other technology or technical field
-Apply the judicial exception with, or by use of, a particular machine - see MPEP
2106.05(b)
-Effect a transformation or reduction of a particular article to a different state or thing -
see MPEP 2106.05(c)
-Apply or use the judicial exception in some other meaningful way beyond generally
linking the use of the judicial exception to a particular technological environment, such that the
claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP
2106.05(e) and Vanda Memo.
Moreover, Examiner views the claims to be merely generally linking the use of the judicial exception to a computer system and generic data.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “acquiring chromatogram data including a plurality of peaks in chromatogram intensity with respect to a predetermined parameter for each of a plurality of reference samples having known attributes;” amounts to mere data gathering as data is just acquired. Examiner further notes that such additional elements are viewed to be well known routine and conventional as evidenced by
Noda (US 20170336370 A1)
Bhardwaj (US 20230086542 A1)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claim fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitations “acquiring chromatogram data including a plurality of peaks in chromatogram intensity with respect to a predetermined parameter for each of a plurality of reference samples having known attributes;” just tie the claim to generic data.
With respect to claim 8,
Step 2A Prong 1:
the following bold limitations are considered abstract:
“a processor configured to acquire mass chromatogram data through chromatogram using a chromatograph mass spectrometer for each of a plurality of reference samples having known attributes;
store peak information included in a mass chromatogram of a standard sample which has a same attribute as each of the reference samples;
store information of a first method for extracting a peak from the mass chromatogram data and a second method that differs from the first method in an algorithm or/and a parameter for extracting a peak;
configured to extract peak information related to the plurality of peaks using the first method for the mass chromatogram data of each of the plurality of reference samples;
configured to determine absence of a peak by collating the peak information extracted by with the peak information of the standard sample having a same attribute as each of the plurality of reference samples;
configured to extract peak information related to the plurality of peaks using the second method for the mass chromatogram data of the reference samples, the mass chromatogram data being determined to have absence of a peak by the processor;
configured to determine absence of a peak by collating the peak information extracted using the second method with the peak information of the standard sample;
configured to acquire a feature amount corresponding to each of the plurality of peaks from the mass chromatogram data determined to have no absence of a peak by the processor to create learning data,
wherein the first method and the second method define a baseline and extract a peak from a waveform of the chromatogram data.”
The above bolded limitations are directed to abstract ideas and would fall within the “Mathematical Concept” and “Mental Process” groupings of abstract ideas. The first method and second methods for extracting peaks are mathematical concepts as seen in Para. [0020] of the specification where it lists that the methods can be a link point method, a horizontal method, a new baseline method, and a minimum chromatogram point method. All of which are well-known mathematical algorithms for determining a peak. According to MPEP 2106.04(C) “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Determining absence of peaks and creating learning data from the found peak information can be done in the human mind using observation, judgement, and opinion.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements –
“a processor configured to acquire mass chromatogram data through chromatogram using a chromatograph mass spectrometer for each of a plurality of reference samples having known attributes;
store peak information included in a mass chromatogram of a standard sample which has a same attribute as each of the reference samples;
store information of a first method for extracting a peak from the mass chromatogram data and a second method that differs from the first method in an algorithm or/and a parameter for extracting a peak.”
Examiner views these limitations amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
As such Examiner does NOT view that the claims
-Improve the functioning of a computer, or to any other technology or technical field
-Apply the judicial exception with, or by use of, a particular machine - see MPEP
2106.05(b)
-Effect a transformation or reduction of a particular article to a different state or thing -
see MPEP 2106.05(c)
-Apply or use the judicial exception in some other meaningful way beyond generally
linking the use of the judicial exception to a particular technological environment, such that the
claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP
2106.05(e) and Vanda Memo.
Moreover, Examiner views the claims to be merely generally linking the use of the judicial exception to a computer system and generic chromatography data.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a processor configured to acquire mass chromatogram data through chromatogram using a chromatograph mass spectrometer for each of a plurality of reference samples having known attributes;
store peak information included in a mass chromatogram of a standard sample which has a same attribute as each of the reference samples;
store information of a first method for extracting a peak from the mass chromatogram data and a second method that differs from the first method in an algorithm or/and a parameter for extracting a peak;” amounts to mere data gathering and using a computer as a tool. As acquiring data through a well-known device is mere data gathering and storing/processing data are well known uses of a generic computer. Examiner further notes that such additional elements are viewed to be well known routine and conventional as evidenced by
Noda (US 20170336370 A1)
Bhardwaj (US 20230086542 A1)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claim fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitations “a processor configured to acquire mass chromatogram data through chromatogram using a chromatograph mass spectrometer for each of a plurality of reference samples having known attributes;
store peak information included in a mass chromatogram of a standard sample which has a same attribute as each of the reference samples;
store information of a first method for extracting a peak from the mass chromatogram data and a second method that differs from the first method in an algorithm or/and a parameter for extracting a peak;” just tie the claim to chromatography.
Dependent claims 2-7 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claims are not directed to an abstract idea, as detailed below:
The dependent claims are directed to further limit the algorithms being used and the way that the data is processed to extract peak information which are mathematical concepts and mental processes. Claims 2 and 3 are directed to displaying data and inputting data into the system which are additional elements, however, they amount to using a computer as a tool.
Therefore, dependent claims 2-7 further limit the abstract idea with an abstract idea and thus the claims are still directed to an abstract idea without significantly more.
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 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Noda (US 20170336370 A1) as modified by Bhardwaj (US 20230086542 A1) and Noda (2019) (US 20190011408 A1).
With respect to claim 1,
Noda teaches,
acquiring chromatogram data including a plurality of peaks in chromatogram intensity with respect to a predetermined parameter for each of a plurality of reference samples having known attributes; (Para. [0002] teaches “In a liquid chromatograph including a multichannel detector such as a PDA detector, an absorption spectrum is repeatedly acquired for sample solution eluted from the outlet of a column, with a time point of injecting a sample into a mobile phase regarded as a starting point, to obtain three-dimensional chromatogram data in three dimensions: time, wavelength, and absorb (signal intensity). In a liquid chromatograph or a gas chromatograph including a mass spectrograph as a detector, namely a liquid chromatograph mass spectrograph or a gas chromatograph mass spectrograph, scan chromatogram is repeated within a predetermined mass-to-charge ratio range using the mass spectrograph to obtain three-dimensional chromatogram data in three dimensions: time, mass-to-charge ratio, and signal intensity (ion intensity).” (i.e. time, mass-to-charge ratio, and signal intensity (ion intensity) are parameters. Para. [0005] teaches “The quantity determination of a known target component” (i.e. known attributes) Para. [0006] teaches “When a peak in question is overlapped with a peak originating from a component other than the target component,”
extracting peak information related to the plurality of peaks using a first method prepared in advance for the chromatogram data of each of the plurality of reference samples; (Para. [0019] teaches “a chromatogram or a spectrum in which peaks originating from sample components are appropriately separated, in an automatic manner, that is, dispensing with inputting entries and the like that involve cumbersome determination by an analyst, even for a peak consisting of a plurality of (three or more) peaks overlapping one another or a peak in the tailing of which a shoulder peak is present.” Para. [0073] teaches “Thus, the EM algorithm for a Gaussian mixture model (GMM) is used here for peak separation on a chromatogram or a spectrum. The EM algorithm is normally an algorithm that repeatedly performs the step of optimizing the parameters of a probability model representing a probability density function of a random variable (i.e., the M step), and the step of optimizing signal separation based on the probability model (i.e., the E step). Modeling is then performed on the assumption that an observation signal is the mixture of a plurality of probability models at their respective concentrations. FIG. 3 illustrates an example of two probability models (models 1 and 2) and a waveform obtained by mixing them.” (i.e. One probability model is the first method))
determining absence of a peak by collating the peak information extracted using the first method with peak information which is prepared in advance of a standard sample which has a same attribute as each of the plurality of reference samples; (Para. [0094] “When the determination in step S4 results in Yes, a residue signal that is left by executing the EM algorithm is obtained, and the presence/absence of a peak-like waveform in the residue signal is determined to judge whether to add a peak model (step S5).” (i.e. adding a peak is seen as collating information))
extracting peak information related to the plurality of peaks using a second method which differs from the first method in an algorithm or/and a parameter for extracting a peak for the chromatogram data of the reference samples, the chromatogram data being determined to have absence of a peak; (Para. [0083] teaches “After the signal is divided to each peak model, as the M step of the EM algorithm, a signal divided to each peak model is subjected to the fitting of a peak model, and model parameters are corrected to increase a likelihood (step S3).” Para. [0097] teaches “Specifically, a spectrum orthogonal to the spectrum of each peak model is extracted from the input chromatogram signal as a residue signal, and the 2-norm of the residue signal is calculated at each retention time. Then, a spectrum residue chromatogram in which the 2-norms of the residue signals are arranged in chronological order is created.” (i.e. second model is used as each peak model is extracted.))
determining absence of a peak by collating the peak information extracted using the second method with the peak information of the standard sample; (Para. [0096] teaches “Specifically, a spectrum orthogonal to the spectrum of each peak model is extracted from the input chromatogram signal as a residue signal, and the 2-norm of the residue signal is calculated at each retention time. Then, a spectrum residue chromatogram in which the 2-norms of the residue signals are arranged in chronological order is created.)
and acquiring a feature amount corresponding to each of the plurality of peaks from the chromatogram data that is extracted using the first method or the second method, (Para. [0090] teaches “that is, first, a suitable initial spectrum is set (step S11), and thereafter, on the assumption that a spectrum is known, the scalar product of the spectrum and a division signal is input, the model parameters of an optimal chromatogram peak common to each wavelength are calculated (step S12).” Para. [0107] teaches “Of course, the magnitude of the eigenvalues of the first to third principal components can be calculated using a feature quantity such as moment, which represents a dispersion of distribution.”)
Noda does not explicitly teach,
and is determined to have no absence of a peak, to create learning data, wherein the first method and the second method define a baseline and extract a peak from a waveform of the chromatogram data.
Bhardwaj teaches,
and is determined to have no absence of a peak, to create learning data. (Para. [0349] teaches “These parameters include, but are not limited to, the presence or absence of one or more peaks, the shape of a peak or group of peaks, the height of one or more peaks, the log of the height of one or more peaks, and other arithmetic manipulations of peak height data.” Para. [0350] teaches “In some embodiments, data derived from the assays (e.g., ELISA assays) that are generated using samples such as “known samples” can then be used to “train” a classification model. A “known sample” is a sample that has been pre-classified. The data that are derived from the spectra and are used to form the classification model can be referred to as a “training data set.” (i.e. training is based on the presence of a 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 Noda with determined to have no absence of a peak, to create learning data such as that of Bhardwaj. One of ordinary skill would have been motivated to modify Noda, because using data with missing peaks could create errors in the classification of the data. Furthermore, Para. [0114] of Noda teaches “learning of the generation model in the GAN and the like is performed by enlarging minute intensity fluctuations. Therefore, it is possible to perform learning with higher precision than when learning the partial waveform itself, and it is possible to improve the calculation precision of the model function and the distribution of the shape parameter of the model function.”
The combination of Noda and Bhardwaj does not explicitly teach,
wherein the first method and the second method define a baseline and extract a peak from a waveform of the chromatogram data.
Noda (2019) teaches,
wherein the first method and the second method define a baseline and extract a peak from a
waveform of the chromatogram data. (Para. [0088] teaches “In the processes described above, the peak chromatogram is calculated by using the baseline estimation result of the chromatogram. However, it is also possible to use the baseline estimation result to select the baseline calculated through various methods and algorithms, instead of performing the process of removing the baseline by using the baseline estimation result as it is.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Noda wherein the first method and the second method define a baseline and extract a peak from a waveform of the chromatogram data such as that of Noda (2019). One of ordinary skill would have been motivated to modify Noda, because correctly extracting a peak is necessary to define a baseline correctly as seen in Para. [0005] of Noda (2019).
With respect to claim 2,
Noda further teaches,
The learning data creation method according to claim 1, wherein the chromatogram data that is determined to have absence of a peak in the peak information extracted using the first method or the second method is displayed on a screen. (Para. [0125] teaches “The data-processing unit 2 is connected to, for example, an input unit 3 for allowing an analyst to specify various parameters necessity for the data processing, and a display unit 4 for displaying peak separation results, quantitative computation results, and the like.”)
With respect to claim 3,
Noda further teaches,
The learning data creation method according to claim 2, wherein peak information is extracted using the second method for chromatogram data designated by a user among the chromatogram data displayed on the screen. (Para. [0126] teaches “an analyst issues instructions to start the execution of the peak separation processing or the like after specifying the data file to be processed on the input unit 3, the peak separation processing unit 23 executes the processing described above using the model function database 22, so as to estimate a chromatogram waveform and a spectrum waveform separated for each component.” (i.e. instructions are viewed as designation))
With respect to claim 4,
Noda further teaches,
The learning data creation method according to claim 1, wherein the chromatogram data is acquired for each of a plurality of target samples having a same attribute, (Para. [0005] teaches “The quantity determination of a known target component” Para. [0037] teaches “Except that use is made of a modified Gaussian distribution into which modification factors including a tailing are incorporated as a model function, the objective of this M step is the same as that of above-described Gaussian distribution M step.”)
chromatogram data of a plurality of reference samples having the same attribute are integrated to each other to create integrated chromatogram data, (Para. [0013] teaches “Thus, it suffices a process for determining such a thing may be added an integrating process for integrating a plurality of peak models may be performed so as to reduce the number of peak models when an excessive division is confirmed.”)
and one acquired by extracting peak information related to a plurality of peaks included in the integrated chromatogram data using the first method is used as the peak information of the standard sample. (Para. [0033] teaches “the qualitative-quantitative analyzer 22 identifies a component (compound) corresponding to each peak, as well as calculates a peak-height value or peak-area value and computes a quantitative value as the concentration or content of each component from that peak-height or peak-area value.” (i.e. peak area and height are seen as peak information))
With respect to claim 5,
Noda further teaches,
The learning data creation method according to claim 1, wherein processing of extracting peak information related to the plurality of peaks using a method having a different algorithm or/and a different parameter to determine absence of a peak is repeated a predetermined number of times until it is determined that there is no absence of a peak. (Para. [0098] teaches “To determine the presence/absence of a peak-like waveform in the spectrum residue chromatogram, various known peak detecting methods can be used, and here, the presence/absence of a peak-like waveform is determined as follows.” Para. [0102] teaches “When the processing returns from step S6 to S2, the EM algorithm by steps S2 to S4 described above is repeated again, with the number of peak models incremented by one. Then, when the peak in question enters the state in which no other component is considered to overlap, the determination in step S5 results in No, the processing is finished, and a chromatogram and a spectrum associated with each component is determined.” (i.e. repeating until all peaks are determined.)
With respect to claim 6,
Noda does not explicitly teach,
wherein the first method and/or the second method extract(s), from among a plurality of chromatogram points constituting the chromatogram data, one having a chromatogram intensity that is lower than the chromatogram intensities of both adjacent chromatogram points, as a minimum chromatogram point,
determine(s) a baseline among the plurality of chromatogram points using the minimum chromatogram point, and extract(s) a peak based on a fact that, at each of the plurality of chromatogram points, a value acquired by subtracting the baseline from the chromatogram intensity of the chromatogram point exceeds a threshold determined in advance.
Noda (2019) teaches,
wherein the first method and/or the second method extract(s), from among a plurality of chromatogram points constituting the chromatogram data, one having a chromatogram intensity that is lower than the chromatogram intensities of both adjacent chromatogram points, as a minimum chromatogram point, (Para. [0084] teaches “Once the score value of each of the partial time ranges is calculated in this manner, the partial time range with the minimum score value is selected as the baseline section. Then, the intensity of each wavelength of the baseline, i.e., the baseline spectrum is determined according to the baseline of the baseline section.”)
determine(s) a baseline among the plurality of chromatogram points using the minimum chromatogram point, (Para. [0084] teaches “Once the score value of each of the partial time ranges is calculated in this manner, the partial time range with the minimum score value is selected as the baseline section. Then, the intensity of each wavelength of the baseline, i.e., the baseline spectrum is determined according to the baseline of the baseline section.”
and extract(s) a peak based on a fact that, at each of the plurality of chromatogram points, a value acquired by subtracting the baseline from the chromatogram intensity of the chromatogram point exceeds a threshold determined in advance. (Para. [0086] teaches “The peak chromatogram in which the baseline is corrected, in other words, from which the influence of the baseline is removed, can be obtained by subtracting the baseline at the wavelength from the chromatogram at each wavelength obtained on the basis of the observation data.” (I.e. see S13 in fig. 5 for predetermined threshold)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Noda wherein the first method and/or the second method extract(s), from among a plurality of chromatogram points constituting the chromatogram data, one having a chromatogram intensity that is lower than the chromatogram intensities of both adjacent chromatogram points, as a minimum chromatogram point, determine(s) a baseline among the plurality of chromatogram points using the minimum chromatogram point, and extract(s) a peak based on a fact that, at each of the plurality of chromatogram points, a value acquired by subtracting the baseline from the chromatogram intensity of the chromatogram point exceeds a threshold determined in advance such as that of Noda (2019). One of ordinary skill would have been motivated to modify Noda, because according to Para. [0038] of Noda (2019) “Consequently, a peak chromatogram which has only a peak and from which the baseline has been accurately removed can be created for each wavelength and each mass-to-charge ratio, enabling highly accurate quantitative determination on the basis of a correct area value of a peak corresponding to each compound, for example.”
With respect to claim 7,
Noda further teaches,
The learning data creation method according to claim 6, wherein both the first method and the second method determine a baseline using the minimum chromatogram point to extract a peak, and the first method and the second method differ from each other in the threshold. (Para. [0003] teaches “They include baseline drift, and an overlap of a plurality of peaks derived from different components caused by insufficient separation.” Para. [0004] teaches “setting an appropriate baseline correction line for a given chromatogram and for appropriately separating overlapping peaks based on the baseline correction line to calculate an integrated area value of each separated peak.” (i.e. baseline minimum method) Para. [0097] teaches “As can be seen by comparing these waveforms, it is possible to obtain a generation model that can output fake data that is very close to real data (substantially indistinguishable) when simulating the calibration curve as well.” (i.e. comparing data from both models is similar but not the same.) Para. [0111] teaches “introducing the partial function into the predetermined function; and outputting a fake waveform to be compared with the input partial waveform by using a parameter of a horizontal axis of the signal waveform as an argument of the function.”)
With respect to claim 8,
Noda teaches,
a processor configured to acquire mass chromatogram data through chromatogram using a chromatograph mass spectrometer for each of a plurality of reference samples having known attributes; (Para. [0002] teaches “In a liquid chromatograph including a multichannel detector such as a PDA detector, an absorption spectrum is repeatedly acquired for sample solution eluted from the outlet of a column, with a time point of injecting a sample into a mobile phase regarded as a starting point, to obtain three-dimensional chromatogram data in three dimensions: time, wavelength, and absorb (signal intensity). In a liquid chromatograph or a gas chromatograph including a mass spectrograph as a detector, namely a liquid chromatograph mass spectrograph or a gas chromatograph mass spectrograph, scan chromatogram is repeated within a predetermined mass-to-charge ratio range using the mass spectrograph to obtain three-dimensional chromatogram data in three dimensions: time, mass-to-charge ratio, and signal intensity (ion intensity).” (i.e. time, mass-to-charge ratio, and signal intensity (ion intensity) are parameters. Para. [0005] teaches “The quantity determination of a known target component” (i.e. known attributes) Para. [0006] teaches “When a peak in question is overlapped with a peak originating from a component other than the target component,” (i.e. plurality of peaks) Fig. 1 shows PDA detector which is seen as acquisition unit. Para. [0129] teaches “In addition, a liquid chromatograph mass spectrometer or a gas chromatograph mass spectrometer including a mass spectrograph as a detector may be employed.” (i.e. a chromatograph mass spectrometer))
store peak information included in a mass chromatogram of a standard sample which has a same attribute as each of the reference samples; (Para. [0092] teaches “and thereafter they are checked against a database in which various modified Gaussian distribution model waveforms are stored.”
store information of a first method for extracting a peak from the mass chromatogram data and a second method that differs from the first method in an algorithm or/and a parameter for extracting a peak; (Para. [0047] teaches “a normal exponential modified Gaussian (EMG) as a chromatogram model waveform, and more preferably, the fitting step may use a database in which chromatogram waveforms each having a peak width, a peak height, and the like that are normalized are stored, and select and use an optimal chromatogram waveform from the database.”)
configured to extract peak information related to the plurality of peaks using the first method for the mass chromatogram data of each of the plurality of reference samples; (Para. [0019] teaches “a chromatogram or a spectrum in which peaks originating from sample components are appropriately separated, in an automatic manner, that is, dispensing with inputting entries and the like that involve cumbersome determination by an analyst, even for a peak consisting of a plurality of (three or more) peaks overlapping one another or a peak in the tailing of which a shoulder peak is present.” Para. [0055] teaches “a contained component determining unit for repeatedly performing processing by the data dividing unit and processing by the fitting unit a specified number of times or until a solution supposedly converges, then filtering the given three-dimensional chromatogram data so as to extract or enhance a spectrum component orthogonal to a spectrum corresponding to each component obtained at a time point,” (i.e. determining unit is viewed as extraction unit) Para. [0073] teaches “Thus, the EM algorithm for a Gaussian mixture model (GMM) is used here for peak separation on a chromatogram or a spectrum. The EM algorithm is normally an algorithm that repeatedly performs the step of optimizing the parameters of a probability model representing a probability density function of a random variable (i.e., the M step), and the step of optimizing signal separation based on the probability model (i.e., the E step). Modeling is then performed on the assumption that an observation signal is the mixture of a plurality of probability models at their respective concentrations. FIG. 3 illustrates an example of two probability models (models 1 and 2) and a waveform obtained by mixing them.” (i.e. One probability model is the first method))
configured to determine absence of a peak by collating the peak information extracted by with the peak information of the standard sample having a same attribute as each of the plurality of reference samples; (Para. [0094] “When the determination in step S4 results in Yes, a residue signal that is left by executing the EM algorithm is obtained, and the presence/absence of a peak-like waveform in the residue signal is determined to judge whether to add a peak model (step S5).” (i.e. adding a peak is seen as collating information) Fig. 1 quantitative computing unit is seen as determination unit.)
configured to extract peak information related to the plurality of peaks using the second method for the mass chromatogram data of the reference samples, the mass chromatogram data being determined to have absence of a peak by the processor; (Para. [0083] teaches “After the signal is divided to each peak model, as the M step of the EM algorithm, a signal divided to each peak model is subjected to the fitting of a peak model, and model parameters are corrected to increase a likelihood (step S3).” Para. [0097] teaches “Specifically, a spectrum orthogonal to the spectrum of each peak model is extracted from the input chromatogram signal as a residue signal, and the 2-norm of the residue signal is calculated at each retention time. Then, a spectrum residue chromatogram in which the 2-norms of the residue signals are arranged in chronological order is created.” (i.e. second model is used as each peak model is extracted.) Fig. 1 shows peak separation processing unit which is viewed as second.)
configured to determine absence of a peak by collating the peak information extracted using the second method with the peak information of the standard sample; (Para. [0096] teaches “Specifically, a spectrum orthogonal to the spectrum of each peak model is extracted from the input chromatogram signal as a residue signal, and the 2-norm of the residue signal is calculated at each retention time. Then, a spectrum residue chromatogram in which the 2-norms of the residue signals are arranged in chronological order is created. Fig 1 shows quantitative computing unit.)
configured to acquire a feature amount corresponding to each of the plurality of peaks from the mass chromatogram data (Para. [0090] teaches “that is, first, a suitable initial spectrum is set (step S11), and thereafter, on the assumption that a spectrum is known, the scalar product of the spectrum and a division signal is input, the model parameters of an optimal chromatogram peak common to each wavelength are calculated (step S12).” Para. [0107] teaches “Of course, the magnitude of the eigenvalues of the first to third principal components can be calculated using a feature quantity such as moment, which represents a dispersion of distribution.”)
Noda does not explicitly teach,
configured to acquire a feature amount corresponding to each of the plurality of peaks from the mass chromatogram data determined to have no absence of a peak by the processor to create learning data, wherein the first method and the second method define a baseline and extract a peak from a waveform of the chromatogram data.
Bhardwaj teaches,
and a learning data creation unit configured to acquire a feature amount corresponding to each of the plurality of peaks from the mass chromatogram data determined to have no absence of a peak by the first determination unit or the second determination unit to create learning data. (Para. [0349] teaches “These parameters include, but are not limited to, the presence or absence of one or more peaks, the shape of a peak or group of peaks, the height of one or more peaks, the log of the height of one or more peaks, and other arithmetic manipulations of peak height data.” Para. [0350] teaches “In some embodiments, data derived from the assays (e.g., ELISA assays) that are generated using samples such as “known samples” can then be used to “train” a classification model. A “known sample” is a sample that has been pre-classified. The data that are derived from the spectra and are used to form the classification model can be referred to as a “training data set.” (i.e. training is based on the presence of a 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 Noda with a processor configured to acquire a feature amount corresponding to each of the plurality of peaks from the mass chromatogram data determined to have no absence of a peak by the processor to create learning data, such as that of Bhardwaj. One of ordinary skill would have been motivated to modify Noda, because using data with missing peaks could create errors in the classification of the data. Furthermore, Para. [0114] of Bhardwaj teaches “learning of the generation model in the GAN and the like is performed by enlarging minute intensity fluctuations. Therefore, it is possible to perform learning with higher precision than when learning the partial waveform itself, and it is possible to improve the calculation precision of the model function and the distribution of the shape parameter of the model function.”
The combination of Noda and Bhardwaj does not explicitly teach,
wherein the first method and the second method define a baseline and extract a peak from a waveform of the chromatogram data.
Noda (2019) teaches,
wherein the first method and the second method define a baseline and extract a peak from a
waveform of the chromatogram data. (Para. [0088] teaches “In the processes described above, the peak chromatogram is calculated by using the baseline estimation result of the chromatogram. However, it is also possible to use the baseline estimation result to select the baseline calculated through various methods and algorithms, instead of performing the process of removing the baseline by using the baseline estimation result as it is.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Noda wherein the first method and the second method define a baseline and extract a peak from a waveform of the chromatogram data such as that of Noda (2019). One of ordinary skill would have been motivated to modify Noda, because correctly extracting a peak is necessary to define a baseline correctly as seen in Para. [0005] of Noda (2019).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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