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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on -03/06/2024, 03/14/2025, 04/09/2025, 07/28/2025- is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-4, 7-12, 16, 17, 20, 21, 22 are rejected under 35 U.S.C 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more.
Specifically, claim 1 recites:
A computer implemented method for automated quality check of chromatographic and/or mass spectral data, the method comprising:
providing processed chromatographic and/or mass spectral data obtained by at least one mass spectrometry device;
classifying a quality of the processed chromatographic and/or mass spectral data by applying at least one trained machine learning model on the chromatographic and/or mass spectral data,
wherein the at least one trained machine learning model uses at least one regression model,
wherein the at least one trained machine learning model is trained on at least one training dataset comprising historical and/or semi-synthetic chromatographic and/or mass spectral data,
wherein the at least one trained machine learning model is an analyte-specific trained machine learning model.
The claim limitations in the abstract idea have been highlighted in bold above.
Under the step 1 of the eligibility analysis, it is determined whether the claims are drawn to a statutory category by considering whether the claimed subject matter fall within the four statutory categories of patentable subject matter identified by 35 U.S.C 101: process, machine, manufacture, or composition of matter. The above claim is considered to be in the statutory category of (process).
Under the step 2A, prong one, it is considered whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into groupings of subject matter when recited as such in a claim limitation, that cover mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental process – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, a step of “classifying a quality of the processed chromatographic and/or mass spectral data by applying at least one trained machine learning model on the chromatographic and/or mass spectral data (is considered to be a mental and mathematical step)
wherein the at least one trained machine learning model uses at least one regression model (is considered to be a mathematical step),
wherein the at least one trained machine learning model is trained on at least one training dataset comprising historical and/or semi-synthetic chromatographic and/or mass spectral data (is considered to be a mathematical step),
wherein the at least one trained machine learning model is an analyte-specific trained machine learning model (is considered to be a mathematical step).
These mental steps represent that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
Similar limitations comprise the abstract ideas of the independent claims 12 and 22.
Next, under the step 2A, prong two, it is considered whether the claim that recites a judicial exception is integrated into a practical application.
In this step, it is evaluated whether the claim recites meaningful additional elements that integrate the exception into a practical application of that exception.
In claim 1, the additional elements/steps is: mass spectrometry device. The above additional elements/steps (hardware) is recited in generality and represent extra solution activity to the judicial exception. The additional element in the preamble of “A computer implemented method…” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “providing processed chromatographic and/or mass spectral…” are also recited in generality which seem to merely be gathering data and not really performing any kind of inventive step to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. All uses of judicial exception require it.
In claim 12, the additional elements/steps recite the similar additional elements/steps as of claim 1. The additional element in the preamble of “A test system for automated…” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “at least one communication interface…” are also recited in generality which seem to merely be gathering data and not really performing any kind of inventive step to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. All uses of judicial exception require it.
In claim 22, the additional element is: a non-transitory computer readable storage medium storing a computer program configured to cause a computer to. The above additional elements/steps (a non-transitory computer readable storage medium storing a computer program configured to cause a computer to – generic computer equipment) are recited in generality and represent extra solution activity to the judicial exception. The additional element in the preamble of “one or more non-transitory machine readable storage…” is not qualified for a meaningful limitation because it is only generally links the use of the judicial exception to a particular technology environment or field of use. The storage medium and the program recited are not qualified as particular machines; a generic computer equipment that is well understood and conventional and is significantly insufficient. The additional elements/steps “receive processed chromatographic…” are also recited in generality which seem to merely be gathering data and not really performing any kind of inventive step to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. All uses of judicial exception require it.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the step 2B.
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.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, the claims 2-4, 7-10, 16, 17, 20, 21 comprise the analogous subject matter and also comprise additional features/steps which are the part of an expanded abstract idea of the independent claims (additionally comprising mathematical relationship/mental process steps) and, therefore, the dependent claims are not eligible without additional elements that reflect a practical application and qualified for significantly more for substantially similar reason as discussed with regards to claim 1, 12 and 22.
Claims 5, 6, 18, 19 and 23 are determined to integrate the abstract ideas into practical application of the invention. Applicant is suggested to include claims 5, 6, 18, 19 and 23 to overcome the rejection under 35 U.S.C 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 7-12, 16, 17, 20, 22 is/are rejected under 35 U.S.C. 102 (a) (1)/(a) (2) as being anticipated by JIARUI DING ET AL: "Feature Selection for Tandem Mass Spectrum Quality Assessment via Sparse Logistical Regression", BIOINFORMATICS AND BIOMEDICAL ENGINEERING 2009. ICBBE 2009. 3RD INTERNATIONAL CONFERENCE ON, IEEE, PISCATAWAY, NJ, USA, 11 June 2009 (2009-06-11), pages 1-4, XP031488645,
ISBN: 978-1-4244-2901-1, herein after “Jiarui”.
Regarding claim 1, Jiarui teaches a computer implemented method for automated quality check of chromatographic and/or mass spectral data, the method comprising: providing processed chromatographic and/or mass spectral data obtained by at least one mass spectrometry device (page 2, 3.1. Consider a training spectral dataset X={xi,yi}N i=1, i=1,...,N Xi ∈ Rd, Yi ∈{−1,1} where xi represents the i-th sample, a d-dimensional feature vector; yi is the class label; and N is the number of training spectra.);
Examiner views the mass spectral data obtained by spectrometer is provided to machine learning. classifying a quality of the processed chromatographic and/or mass spectral data by applying at least one trained machine learning model on the chromatographic and/or mass spectral data (page 3, right col, second paragraph To test the effectiveness of the selected features, a logisti cal regression classifier is trained using only the 10 selected features. Table 3 shows the performance of the classifier in terms of the area under the receiver operating characteristic curve (AUC) and the true negative rate (TNR). In [13], logistical regression was also used for quality assessment of tandem mass spectra, and the results were very good.),
Examiner views, based on the spectral data a quality of the mass spectral data is classified using a trained machine learning model
wherein the at least one trained machine learning model uses at least one regression model (page 3, section 4. 3, Right col. second paragraph. In this study, we also construct their nine features, and construct a logistical regression model for quality assessment of tan dem mass spectra.),
wherein the at least one trained machine learning model is trained on at least one training dataset comprising historical and/or semi-synthetic chromatographic and/or mass spectral data, (page 2, 4.1, right col. This study employs two tandem mass spectral datasets: ISB dataset and TOV dataset to investigate the performance of the sparse logistical regression method.)
Examiner views the ISB and TOV dataset were searched (i.e., historical) spectral data set used to train machine learning model in this study.
wherein the at least one trained machine learning model is an analyte-specific trained machine learning model (page 1, section 1 introduction: Tandem mass spectrometers are powerful tools for the analysis of biological complexes. In a typical tandem mass spectrometry experiment, proteins are first extracted from a biological complex. Then after a protein is digested into peptides by proteases like trypsin, a tandem mass spectrometer measures the intensities of peptide ions and fragment ions verse their mass to charge ratio (m/z) which are called mass spectra.).
Examiner views the machine learning used is for a protein (i.e., analyte-specific) trained machine learning model.
Regarding claim 2, Jiarui teaches the method of claim 1, wherein the analyte is at least one target substance selected from the group consisting of vitamin D, drugs of abuse, therapeutic drugs, hormones, and metabolites which shall be quantified from a sample (page 1, section 1 introduction: Tandem mass spectrometers are powerful tools for the analysis of biological complexes. In a typical tandem mass spectrometry experiment, proteins are first extracted from a biological complex. Then after a protein is digested into peptides by proteases like trypsin, a tandem mass spectrom eter measures the intensities of peptide ions and fragment ions verse their mass to charge ratio (m/z) which are called mass spectra.).
Examiner views protein as an analyte sample which has peptide ion as a metabolite which shall be quantified from the analyte sample.
Regarding claim 3, Jiarui teaches the method of claim 1, wherein the at least one regression model is at least one regression model selected from the group consisting of a Random Forest; a Gradient Boosting forest; a Partial Least Squares, a Lasso regression; a Logistic regression; and a Bayesian regression (page 3, section 4. 3, Right col. second paragraph. In this study, we also construct their nine features, and construct a logistical regression model for quality assessment of tan dem mass spectra.).
Regarding claim 4, Jiarui teaches the method of claim 1, wherein providing the processed chromatographic and/or mass spectral data comprises automatically providing the processed chromatographic and/or mass spectral data obtained by the at least one mass spectrometry device (abstract: Machine learning algorithms are widely used for quality assessment of tandem mass spectra based on a number of features);
Examiner views the machine learning automatically processes the spectral data obtained by the spectrometer. and
wherein classifying the quality of the processed chromatographic and/or mass spectral data comprises automatically classifying the quality of the processed chromatographic and/or mass spectral data by applying the at least one trained machine learning model on the chromatographic and/or mass spectral data (page 2, section 4.1 This study employs two tandem mass spectral datasets:ISB dataset and TOV dataset to investigate the performance
of the sparse logistical regression method.
page 3, first paragraph, All these 3592 spectra were labeled as “high” quality, and all the other spectra in the dataset were labeled as “poor” quality in this study.).
Examiner views the trained machine learning using regression method automatically classifies the processed mass spectral data as a high or a poor quality data.
Regarding claim 7, Jiarui teaches the method of claim 1, wherein the at least one trained machine learning model uses a feature set that comprises at least one feature selected from the group consisting of a peak area, a peak background, a relative background, an ion ratio, a Q4 ratio, a retention time ratio, a peak asymmetry, an asymmetry ratio, a peak width, a peak width ratio, an area of integration residuals, a confidence interval of peak area, a mass shift, a full width half maximum, a signal to noise ratio, a single cycle ratio median, a single cycle ion ratio median, a peak height, a peak fit mean squared error, a fit-intensity correlation, and an Earth Mover's Distance (para (page 1, section 1 introduction: Tandem mass spectrometers are powerful tools for the analysis of biological complexes. In a typical tandem mass spectrometry experiment, proteins are first extracted from a biological complex. Then after a protein is digested into peptides by proteases like trypsin, a tandem mass spectrometer measures the intensities of peptide ions and fragment ions verse their mass to charge ratio (m/z) which are called mass spectra.).
Examiner views the Intensities of peptide ions, ratio are some of the features selected to be used for machine learning.
Regarding claim 8, Jiarui teaches the method of claim 1, further comprising training the at least one trained machine learning model based on the at least one training dataset (page 3, left col. section 4.2 performance evaluation: second paragraph The ISB dataset is used for features selection since it is a standard dataset and has been intensively studied. The ISB dataset is first divided into 2 equal size training and testing data, and the training data is used for model selection and feature selection. A number of training subsets are constructed from the training data for feature selection.).
Examiner views the subset training data are created for training a machine learning model.
Regarding claim 9, Jiarui teaches the method of claim 8, wherein training the at least one trained machine learning model comprises training the at least one trained machine learning model for different analytes (Abstract: Machine learning algorithms are widely used for quality assessment of tandem mass spectra based on a number of features. page 1. Left col. introduction: Tandem mass spectrometers are powerful tools for the analysis of biological complexes. In a typical tandem mass spectrometry experiment, proteins are first extracted from a biological complex.).
Examiner views the created training a machine learning model for different biological complexes, proteins (i.e., different analytes)
Regarding claim 10, Jiarui teaches the method of claim 1, wherein the at least one training dataset is generated by manual classification of the historical and/or semi-synthetic chromatographic and/or mass spectral data into two categories (page 2, right col. 4.1 Datasets: This study employs two tandem mass spectral datasets: ISB dataset and TOV dataset to investigate the performance of the sparse logistical regression method).
Examiner views the training a machine learning model’s training data is selected using two recorded tandem mass spectral datasets (i.e., manual classification of historical)
Regarding claim 11, Jiarui teaches the method of claim 1, wherein the semi-synthetic chromatographic and/or mass spectral data comprises modified historical chromatographic and/or mass spectral data, wherein the historical chromatographic and/or mass spectral data is modified by one or more of introducing at least one interference, introducing background, introducing at least one shift in retention time, modifying peak width, and/or replacing an internal standard signal by a chromatogram from a double blank sample ((page 2, right col. 4.1 Datasets: This study employs two tandem mass spectral datasets: ISB dataset and TOV dataset to investigate the performance of the sparse logistical regression method)….page 3 left col. line 1 These data were also analyzed by InsPecT, and annotated another 820 possibly modified (mutated) peptides [12].).
Examiner views the training a machine learning model’s training data is selected using two recorded tandem mass spectral datasets (i.e., manual classification of historical). The data set were modified or mutated (i.e., for example modifying the peak width or replacing signal).
Claim 12 is rejected as claim 1 having same/similar claim limitation.
Claim 16 is rejected as claim 8 having same/similar claim limitation.
Claim 17 is rejected as claim 9 having same/similar claim limitation.
Claim 20 is rejected as claim 3 having same/similar claim limitation.
Claim 22 is rejected as claim 1 having same/similar claim limitation.
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.
Claim(s) 5, 6, 18, 19, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiarui in view of BRETT G. AMIDAN ET AL: "Signatures for Mass Spectrometry Data Quality", JOURNAL OF PROTEOME RESEARCH, vol. 13, no. 4, 24 March 2014 (2014-03-24), pages 2215-2222, XP055529723, ISSN: 1535-3893, DOI: 10.1021/pr401143e, herein after “Brett”.
Regarding claim 5, Jiarui teaches the method of claim 1, Jiarui does not clearly teach wherein the classified quality is used for distinguishing between acceptable and non-acceptable chromatographic and/or mass spectral data; and wherein the method further comprises assigning a flag to the chromatographic and/or mass spectral data as acceptable or non-acceptable based on the classified quality.
Brett teaches wherein the classified quality is used for distinguishing between acceptable and non-acceptable chromatographic and/or mass spectral data (page 1, Introduction, Determining whether an instrument is operating within acceptable performance metrics is an essential step during scientific investigation… In addition to regularly running a quality control (QC) sample, recent research has investigated analysis methods and metrics for assessing data quality); and wherein the method further comprises assigning a flag to the chromatographic and/or mass spectral data as acceptable or non-acceptable based on the classified quality (page 2217, left col. expert annotation, line 6. In the first round, 1150 data sets were manually curated as “good”, “okay”, or “poor” and used to develop the classifier.
page 2218, left col, first paragraph. In an effort to automate the assessment of QC data sets, we selected a testing/training sample of 1150 data sets (see Table 1) and manually annotated them as being “good,” “OK”, or “poor.”).
Examiner views the color codes for good, poor and ok as assigning a flag to mass spectral data as being acceptable (i.e., green-good or blue-ok) and unacceptable (i.e., red-poor) based on the classified quality.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Brett into Jiarui for the purpose of classifying the spectral data as acceptable or unacceptable using an assigned flag or color, so the quality control of the device can be properly recorded or maintained.
Regarding claim 6, the combination of Jiarui and Brett teaches the method of claim 5, Brett teaches further comprising providing at least one information depending on the flag of the chromatographic and/or mass spectral data to a user via at least one user-interface (Page, 2217.left col. Expert Annotations. Line 1. The data sets were manually reviewed by three expert instrument operators (30+ years of combined LC−MS experience) using an in-house graphical user interface viewer.).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Brett into Jiarui for the purpose of classifying the spectral data as acceptable or unacceptable using an assigned flag or color, so the quality control of the device can be properly recorded and studied using a graphical user interface.
Claims (18, 23) and 19 are rejected as claims 5 and 6 respectively having similar claim limitations.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiarui in view of Noto et al, “FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection. Data Mining and Knowledge Discovery, 25. 109-133 (2012)” herein after “Noto”.
Regarding claim 21, Jiarui teaches the method of claim 7, Jiarui does not clearly teach wherein the feature set comprises a deviation of a feature derived from processed data and raw data.
Noto teaches wherein the feature set comprises a deviation of a feature derived from processed data and raw data (page 5, last paragraph, we define the error of a numeric feature as the difference between an observed and a predicted value, xqi – Ci (ρi (xq)).).
Here examiner views the feature value or set is derived from the predicted (i.e., processed) and observed (i.e., raw data).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Noto into Jiarui for the purpose of determining a feature value by using predicted data and observed so that the error or variance influence of the observed data in the modeling can be accurately determined and minimized.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lubkowitz et al US 20060125826 A1 discusses managing and analyzing mass spectrometer data.
Guetter et al US 12461077 B2 discusses analyzing mass spectrometer data using machine learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAD TIMILSINA whose telephone number is (571)272-7104. The examiner can normally be reached Monday-Friday 9:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at 571-270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SHARAD TIMILSINA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
Prosecution Insights