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. DETAILED ACTION Status of Claims Claim 21 is rejected under 35 USC § 1 01 Rejection. Claims 1-19 are Objected claims. Claim 20 is canceled claim. Objection In claims 1 , 7, 13, 16 and 21 refer to the acronym “NMF”, which is not permissible. The acronym should be spell ed out in claims 1 , 7, 13, 16 and 21 . Appropriate correction is required. Claim Rejections - 35 USC §101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter . Claim 21 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim is drawn to a computer program per se , which is no n-statutory (see MPEP 2106.03). Allowable Subject Matter Claims 1-19 are objected claims, because the claims 1, 7, 13 , 16 and 21 re cite acronyms, but claims 1- 19 would be allowable if the acronym s w ere spell ed out in the claims. 1) Examiner note regarding the prior art of the record: 1. Nakamura (US Pub.20170356889) disclose performing an analysis of the difference between a specific sample group and a nonspecific sample group, a principle component analysis processing unit (33) performs principle component analysis on a collection of a plurality of mass spectrums created from data obtained for a single specific sample. The similarity representative value for each sample is obtained for all the characteristic spectrums. A difference determination unit (36) checks whether there is a significant difference between the distribution of the similarity representative values of the specific sample group and the distribution of the similarity representative values of the nonspecific sample group and determines that the characteristic spectrum which is the source of the similarities having a significant difference is a difference spectrum. (Abstract) . 2. Nakakimura (WO2018158801A1) disclose each sample, representative similarity values are determined for each characteristic spectrum. A difference spectrum determination unit (36) checks a histogram indicating the number of samples for each representative similarity value level for significant differences between the distributions corresponding to each sample group and determines that a characteristic spectrum that is a source of similarities having a significant difference is a difference spectrum (abstract). 3. Vitaletti (US 20130073219A1) disclose selecting from each said mass spectrum data file other than the first mass spectrum data file, peak coordinates which are close to the read peak coordinates from the first mass spectrum, by computing a distance function qualifying a proximity between two peaks (para [0011]). 4 . Young (US Pub.2007288174A1) disclose a system is provided for analyzing metabolomics data received from an analytical device across a group of samples. The system automatically receives a data matrix corresponding to each of the samples, wherein the data matrix includes rows corresponding to each of the samples and columns corresponding to a group of ions present in the respective samples. A processor is provided for determining a characteristic value corresponding to at least one of a group of components present in the samples, wherein the components are made up of at least a portion of the group of ions, using at least one of a correlation function and a factorization function (Abstract). 5 . Meija (US Pub.20090121125A1) disclose a method of obtaining pure component mass spectra or pure peak elution profiles from mass spectra of a mixture of components involves estimating number of components in the mixture, filtering noise, and extracting individual component mass spectra or pure peak elution profiles using blind entropy minimization with direct optimization (e.g. downhill simplex minimization)(Abstract). Regarding claim 1: The prior art s of record does not teach or fairly suggest a method of having the steps: “ preparing learning samples of a number equal to or greater than K containing at least one type of component selected from the K types of components and having compositions different from each other, and a background sample not containing the component; sequentially ionizing gas components generated by thermal desorption and/or pyrolysis while heating each sample in a sample set including the inference target sample, the learning samples, and the background sample, and observing mass spectra continuously; storing the mass spectrum acquired for each heating temperature into each row to acquire two-dimensional mass spectra of the respective samples, and merging at least two or more of the two-dimensional spectra and converting the spectra into a data matrix; performing NMF process by which the data matrix is subjected to non-negative matrix factorization to be factorized into the product of a normalized base spectrum matrix and a corresponding intensity distribution matrix; extracting a noise component in the intensity distribution matrix through analysis on canonical correlation between the base spectrum matrix and the data matrix, and correcting the intensity distribution matrix so as to reduce influence by the noise component, thereby acquiring a corrected intensity distribution matrix; partitioning the corrected intensity distribution matrix into a submatrix corresponding to each of the samples, and expressing each of the samples in vector space using the submatrix as a feature vector; defining a K-1 dimensional simplex including all of the feature vectors and determining K end members in the K-1 dimensional simplex; and calculating a Euclidean distance between each of the K end members and the feature vector of the inference target sample, and inferring a content ratio of the component in the inference target sample on the basis of a ratio of the Euclidean distance, wherein if the K is equal to or greater than 3, at least one of the feature vectors of the learning samples is present in each region external to a hypersphere inscribed in the K-1 dimensional simplex or the learning samples contain at least one of the end members " of the invention as in claims 1- 21 . As a result of the configuration, the present invention provides an advantageous effect of easily allowing quantitative analysis with high accuracy. Claims 2-12 are not rejected under 102/103 rejection as being dependent from base claim 1. Regarding claim 13: The prior art s of record does not teach or fairly suggest a method of having the steps of: “merges at least two or more of the two-dimensional spectra and converts the spectra into a data matrix; an NMF processing part that performs NMF process by which the data matrix is subjected to non-negative matrix factorization to be factorized into the product of a normalized base spectrum matrix and a corresponding intensity distribution matrix; a correction processing part that extracts a noise component in the intensity distribution matrix through analysis on canonical correlation between the base spectrum matrix and the data matrix, and corrects the intensity distribution matrix SO as to reduce influence by the noise component, thereby generating a corrected intensity distribution matrix; a vector processing part that partitions the corrected intensity distribution matrix into a submatrix corresponding to each of the samples in the sample set, and expresses each of the samples in vector space using the submatrix as a feature vector; an end member determining part that defines a K-1 dimensional simplex including all of the feature vectors and determines K end members in the K-1 dimensional simplex; and a content ratio calculating part that calculates a Euclidean distance between each of the K end members and the feature vector of the inference target sample, and infers a content ratio of the component in the inference target sample on the basis of a ratio of the Euclidean distance, and if the K is equal to or greater than 3, at least one of the feature vectors of the learning samples is present in each region external to a hypersphere inscribed in the K-1 dimensional simplex or the learning samples contain at least one of the end members”. Claims 14- 19 are not rejected under 102/103 rejection as being dependent from base claim 1 3 . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT KALERIA KNOX whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-5971 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 8am-5pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT Andrew Schechter can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571)2722302 . 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