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 .
Information Disclosure Statement
The IDS filed on 04/10/24 has been considered.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: P1, P2. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a waveform-analyzing method, which is a process. Independent claim 11 is directed to a waveform-analyzing device, which is a machine. All other claims depend on independent claims 1 and 11. As such, claims 1-21 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
A waveform-analyzing method for analyzing a signal waveform which is a chromatogram or a spectrum (The claimed waveform-analyzing method recites abstract mathematical concepts and/or mental processes, as seen below), the method comprising:
a model creation step for creating a trained model for locating a peak portion in an input waveform, by machine learning using a plurality of sets of partial waveforms prepared by dividing each reference waveform in which a position of the peak portion is already known (Details of the trained model are not claimed here. The general operation, of preparing a set of partial waveforms by dividing a reference waveform in which a position of the peak portion is already known, is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. For example, the human mind is able to process and execute the operations shown in figures 4-9 of the applicant’s drawings. The limitation therefore recites an abstract mental process. It is possible that the creation of a more sophisticated trained model (which is not claimed) may not be able to be performed in the human mind. However, such sophisticated models are often defined by specific mathematical calculations. For example, paragraph 0023 of the applicant’s original specification states, “the task of creating a trained model normally requires a huge amount of calculation.”)
a region estimation step which includes dividing an analysis-target waveform into a plurality of partial waveforms, determining whether or not a partial waveform is a peak portion for each of the plurality of partial waveforms by using the trained model, and estimating a plurality of different kinds of regions including a single-peak region, overlap-peak region and non-peak region in the analysis-target waveform based on a result of the determination (The claimed estimation and determination limitations disclose abstract mental processes that can be performed in the human mind. As discussed above, a general conception of dividing a waveform into a plurality of partial waveforms is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind, as shown in figures 4-9 of the applicant’s drawings. A simple determination, of whether a partial waveform is a peak portion, is also an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. Similarly, estimating which type of region, among a limited subset, most corresponds to a determination, is also an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. In addition, based on the disclosure of the applicant’s original specification, the claimed limitation appears to be performed by specific mathematical calculations. For example, paragraph 0037 of the applicant’s original specification states, “the determiner 211 using the trained model calculates certainty information for each partial waveform and each kind of region, where the certainty information is a numerical value representing the probability that the partial waveform concerned corresponds to the kind of region concerned (Step S13).”)
a multimodality determination step which includes determining, for an overlap peak within a region estimated to be an overlap-peak region in the region estimation step, whether or not the overlap peak is a multimodal peak originating from a single component, using at least one of following pieces of information: a height of one or more peaks among a plurality of peaks in the overlap peak; a depth of a trough between two neighboring peaks among the plurality of peaks; and a width in a direction of a horizontal axis between a bottom portion of the trough and a top portion of one of the peaks between which the trough is sandwiched (A determination of a binary decision, such as whether or not the overlap peak is a multimodal peak originating from a single component, is an abstract mental process that can be performed in the human mind. In addition, based on the disclosure of the applicant’s original specification, the claimed determination appears to recite specific mathematical calculations. For example, paragraph 0019 of the applicant’s original specification states, “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 …”)
Independent claim 11 represents a device claim variation of method claim 1. It recites similar abstract limitations, for the reasons discussed above.
Dependent claims 2-10 and 12-21 depend on independent claims 1, 10, and 18; they also recite the independent claims’ abstract limitations, by virtue of their dependence. In addition, some of the claims also recite their own abstract mathematical concepts and/or mental processes.
Claims 2 and 12
further comprising an integration step for integrating a peak identified as a multimodal peak in the multimodality determination step so as to allow the multimodal peak to be treated as a single peak (This limitation recites an abstract mental process. Figure 8 and paragraph 0049 of the applicant’s original specification describes the integration process. Paragraph 0049 states, “If the peak shown in section (A) in Fig. 8 has been identified as a vertical-partitioning peak … Then, the multimodal peak integrator 216 deletes the peak-beginning and peak-ending regions … and replaces the entire portion between the first peak-beginning region and the second peak-ending region with a single-peak region …” This is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.)
Claims 3 and 13
wherein whether or not the integration by the integration step should actually be performed is selected according to a user’s selection (This limitation recites an abstract mental process. Making a binary decision, based on a general and generic user’s selection, is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.)
Claims 4 and 14
wherein the overlap-peak region is subdivided into a plurality of kinds of regions according to a method for dividing the overlap peak, including a vertical partitioning peak region, and the multimodality determination step includes making a determination on a peak corresponding to a vertical partitioning peak region as to whether or not the peak is a multimodal peak (As discussed above, subdividing an overlap-peak region into a plurality of kinds of regions is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. Similarly, making a binary determination of whether a peak is a multimodal peak or not, is an observation, evaluation, judgment, and/or opinion.)
Claims 5 and 15
wherein one of conditions applied in the multimodality determination step for determining that a peak concerned is a multimodal peak is that a height of a main peak or a shoulder peak is not greater than a predetermined threshold (The presence of a mathematical threshold represents a specific mathematical relationship. This limitation therefore recites abstract mathematical concepts. Furthermore, a binary decision, based on whether a value is greater than or less than a simple threshold value, is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.)
Claims 6 and 16
wherein the multimodality determination step includes making a determination on the multimodal peak by using at least one of following values: a ratio between a height of a main peak and a height of a shoulder peak; a ratio between a depth of a trough between two peaks neighboring each other and a height of one of the two peaks; and a width of a portion between a bottom portion of the trough and a top portion of one of the two peaks (This limitation recites specific mathematical relationships, such as ratios. Also, making a determination based on simple numerical values, such as a ratio, is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.)
Claims 7 and 17
wherein the multimodality determination step includes calculating one or more of following values in the overlap-peak region: a signal-to-noise ratio, a degree of separation, a symmetry factor, an area and a peak width, and using the calculated result for the determination on the multimodal peak as well (This limitation explicitly recites mathematical calculations. The limitation therefore recites abstract mathematical concepts.)
Claims 10 and 20
wherein the multimodality determination step uses a change in waveform before and after a smoothing process on an overlap-peak waveform when making a determination on the multimodal peak (The change (or difference) in a waveform represents an abstract mathematical relationship between before and after conditions. The limitation therefore recites an abstract mathematical concept.)
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claims 8 and 18
wherein the signal waveform is a chromatogram acquired by chromatograph mass spectrometry, and the multimodality determination step uses both a determination result for a chromatogram of a target ion and a determination result for a chromatogram of a qualifier ion for a same component to make a determination on the multimodal peak (The disclosure of a chromatogram acquired by chromatograph mass spectrometry merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Using the contextual data of the field of use in the mathematical and/or mental determinations merely add general context to claims that are directed to processing data. The structural details of the chromatogram and/or chromatograph mass spectrometry are not positively recited.)
Claims 9 and 19
wherein the multimodality determination step uses compound information related to a target compound for a determination on the multimodal peak (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and/or processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
Examiner’s Note - Allowable Subject Matter
Please note that the claims cannot be allowed until the above 101 rejection is overcome.
With respect to independent claim 1, the below limitations were not found, taught, suggested, or disclosed in the prior art.
a region estimation step which includes dividing an analysis-target waveform into a plurality of partial waveforms, determining whether or not a partial waveform is a peak portion for each of the plurality of partial waveforms by using the trained model, and estimating a plurality of different kinds of regions including a single-peak region, overlap-peak region and non-peak region in the analysis-target waveform based on a result of the determination; and
a multimodality determination step which includes determining, for an overlap peak within a region estimated to be an overlap-peak region in the region estimation step, whether or not the overlap peak is a multimodal peak originating from a single component, using at least one of following pieces of information: a height of one or more peaks among a plurality of peaks in the overlap peak; a depth of a trough between two neighboring peaks among the plurality of peaks; and a width in a direction of a horizontal axis between a bottom portion of the trough and a top portion of one of the peaks between which the trough is sandwiched
With respect to independent claim 11, the below limitations were not found, taught, suggested, or disclosed in the prior art.
a region estimator configured to divide an analysis-target waveform into a plurality of partial waveforms, to determine whether or not a partial waveform is a peak portion for each of the plurality of partial waveforms of the analysis-target waveform, by using a trained model created by machine learning using a plurality of sets of partial waveforms prepared by dividing each reference waveform in which a position of the peak portion is already known, and to estimate a plurality of different kinds of regions including a single-peak region, overlap-peak region and non-peak region in the analysis-target waveform based on a result of the determination; and
a multimodality determiner configured to determine, for an overlap peak within a region estimated to be an overlap-peak region by the region estimator, whether or not the overlap peak is a multimodal peak originating from a single component, using at least one of following pieces of information: a height of one or more peaks among a plurality of peaks in the overlap peak; a depth of a trough between two neighboring peaks among the plurality of peaks; and a width in a direction of a horizontal axis between a bottom portion of the trough and a top portion of one of the peaks between which the trough is sandwiched
All other claims depend on independent claims 1 and 11. They also disclose limitations that were not found, taught, suggested, or disclosed by the prior art, as a result of their dependency.
The most relevant piece of art found was Fujita et al (US PgPub 20210319364).
With respect to claim 1, Fujita et al discloses:
A waveform-analyzing method for analyzing a signal waveform which is a chromatogram or a spectrum (figure 1; abstract; Paragraph 0056 states, “Data to be analyzed in the present example is three-dimensional GCMS data acquired by measurement using a gas chromatograph mass spectrometer. In addition, in the present example, an AMDIS is used as an analysis program, and a mass spectrum, purified by separating a peak of a waveform (TICC waveform) of a total ion current chromatogram obtained from GCMS data …”), the method comprising:
a model creation step for creating a trained model for locating a peak portion in an input waveform, by machine learning using a plurality of sets of partial waveforms prepared by dividing each reference waveform in which a position of the peak portion is already known (figure 2, references S4-S7; paragraphs 0065-0073; paragraph 0073 states, “FIGS. 7A to 7C are overlaid illustrations of TICC waveforms of the divided reference data associated with the learning parameter set …” Under broadest reasonable interpretation, waveforms of divided data can arguably be broadly construed to serve as a dividing of a reference waveform into partial waveforms.)
However, Fujita et al does not explicitly disclose the details of the claimed region estimation step and multimodality determination step. A similar rationale applied to independent claim 11.
As seen in the pertinent prior art section below, a number of references were found that relate to the claimed invention. Some of the art attempts to solve similar problems. However, the examiner determined that the cited art, even if solving similar problems, did so in a different manner than the detailed manner that was claimed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Monyai, K.; van Zyl, T.; and Stoychev, S. – “Peak Detection, Feature Extraction and Clustering of Peptides Fragments Ions”; 2019 6th Intl. Conference on Soft Computing & Machine Intelligence.
Wang et al (US PgPub 20200302187) discloses a method, apparatus, and system for people counting and recognition based on rhythmic motion monitoring.
Hauschild, A.; Kopczynski, D.; D’Addario, M.; Baumback, J.I.; Rahmann, S.; and Baumbach, J. – “Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches”; Metabolites 2013, 3, 277-293.
Hauschild, A; Schneider, T.; Pauling, J.; Rupp, K.; Jang, M.; Baumbach, J.I.; and Baumbach, J. – “Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data – Reviewing the State of the Art”; Metabolites 2012, 2, 733-755.
Serie (US PgPub 20200372973) discloses automated detection of boundaries in mass spectrometry data.
Souza, B.; Lopes-dos-Santos, V.; Bacelo, J.; and Tort, A. – “Spike sorting with Gaussian mixture models”; Scientific Reports (2019) 9:3627.
Su, C.; Chiang, C.; Lee, C.; Fan, Y.; Ho, C.; and Shyu, L. – “Computational solution of spike overlapping using data-based subtraction algorithms to resolve synchronous sympathetic nerve discharge”; Frontiers in Computational Neuroscience; October 2013, Volume 7, Article 149.
Zheng, P.; Carlin, S.; Franceschi, P.; Mattivi, F.; and Vrhovsek, U. – “Application of a Target-Guided Data Processing Approach in Saturated Peak Correction of GCxGC Analysis”; Anal. Chem. 2022, 94, 1941-1948.
Ivosev (WO 2016125059 A1) discloses interference detection and peak of interest deconvolution.
Du, Xiuxia and Zeisel, Steven; “Spectral Deconvolution for Gas Chromatography Mass Spectrometry-Based Metabolomics: Current Status and Future Perspectives”; Computational and Structural Biotechnology Journal; Volume No: 4, Issue 5, January 2013.
Hoffmann, N.; Keck, M.; Heuweger, H.; Wilhelm, M.; Hogy, P.; Niehaus, K.; and Stoye, J. – “Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets.”; BMS Bioinformatics 2012, 13:214.
Zhang, W. and Zhao, P. – “Quality evaluation of extracted ion chromatograms and chromatographic peaks in liquid chromatography/mass spectrometry-based metabolomics data”; BMC Bioinformatics 2014, 15(Suppl 11): S5.
O’Callaghan, Sean; De Souza, D.; Isaac, A.; Wang, Q.; Hodkinson, L.; Olshansky, M.; Erwin, T.; Appelbe, B.; Tull, D.; Roessner, U.; Bacic, A.; McConville, M.; and Likic, V. – “PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools.”; BMC Bioinformatics 2012, 13:115.
Pirttila, K.; Balgoma, D.; Rainer, J.; Pettersson, C.; Hedeland, M.; and Brunius, C. – “Comprehensive Peak Characterization (CPC) in Untargeted LC-MS Analysis”; Metabolites 2022, 12, 137.
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/LEONARD S LIANG/Examiner, Art Unit 2857 06/25/26
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