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. Claim Status Claims 1-13 are currently pending and under exam herein. Claims 1-13 are rejected. Claims 3, 8, and 11 are objected to. Priority Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of the foreign priority application was received on July 21 , 2022 . Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. At this point in the examination, the effective filing date of claim s 1-13 is June 29, 2021 . Information Disclosure Statement The information disclosure statement (IDS) submitted on June 27 , 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Drawings The drawings filed on June 27, 2022 are accepted. Specification The disclosure is objected to because of the following informalities: There are several instances that describe training the algorithm classifier on normalized reference mass spectra, such as in paragraph 0007 that are grammatically incorrect because the way it is written states that the data is being trained . There are several instances that describe a spectrum conversion step or unit, such as in paragraph 0008 where a “mass-to-charge ratio conversing method” is employed. It appears to be a typo and the word “conversing” should be changed to “conversion” in each occurrence . The use of the term s Sepsityper ®, Vitek ®, and BACpro ® in paragraphs 0027, 0048, and 0086 , which a re trade name s or mark s used in commerce, has been noted in this application. The term s should be accompanied by the generic terminology; furthermore, the term s should be capitalized wherever they appear or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term s . Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks. The proper symbol needs to be added to Vitek in paragraph 0027 and removed from the word kit. Appropriate correction is required. Claim Objections Claim s 3, 8, and 11 are objected to because of the following informalities: The word “conversing” in the phrase “mass-to-charge conversing method” needs to be cha n ged to “conversion” . Appropriate correction is required. Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: Claim 11: “ A calibration unit for removing background noise background noise of the target mass spectrum data so as to obtain a first processed target mass spectrum data. ” “ A sampling normalization uni t signally connected to the calibration unit , wherein the sampling normalization unit is for adjusting a temporal resolution value of the first processed target mass spectrum data so as to obtain a second processed target mass spectrum data. ” Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof . For the limitation s of a calibration unit and a sampling normalization unit, the structure described in paragraphs 0029-0031, 0055 and 0066 -0067 will be used. If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitations recite sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitations are: Claim 1: “ Performing a model establishing step , comprising: … ” “ Performing a reference spectrum pre-processing step, wherein the reference mass spectrum data are pre-processed so as to obtain a plurality of normalized reference mass spectrum data . ” “ Performing a model training step, wherein the normalized reference mass spectrum data are trained to achieve a convergence by an algorithm classifier so as to obtain an antibiotic resistance assessing classifier .” “P erforming a sample pre-processing step, wherein the test sample is processed by the conventional sample processing method or the rapid sample processing method so as to obtain a processed sample .” “ Performing an analysis step, wherein the processed sample is detected by a mass spectrometry method so as to obtain a target mass spectrum data .” “ Performing a spectrum pre-processing step, wherein the target mass spectrum data is pre-processed so as to obtain a normalized target mass spectrum data .” “ Performing a feature extraction step, wherein the normalized target mass spectrum data is trained to achieve a convergence by the antibiotic resistance assessing classifier so as to obtain a spectrum feature .” “ Performing an assessing step, wherein the spectrum feature is analyzed by the antibiotic resistance assessing classifier so as to output an assessed result of drug-resistant microorganism, and the assessed result of drug-resistant microorganism is for assessing whether the test microorganism is a drug-resistant microorganism or not .” Claim 2: “ P erforming a centrifuging step, wherein the test sample is processed by a plurality of centrifugations so as to obtain a centrifuged sample, and the centrifuged sample comprises the test microorganism . ” “ Performing a reactive step, wherein a reaction reagent is added to the centrifuged sample and then well mixed so as to obtain a post-reaction sample .” “ Performing a final centrifuging step, wherein the post-reaction sample is centrifuged so as to obtain the processed sample .” Claim 3: “ Performing a calibration step, wherein a background noise of the target mass spectrum data is removed so as to obtain a first processed target mass spectrum data .” “ P erforming a sampling normalization step, wherein a temporal resolution value of the first processed target mass spectrum data is adjusted so as to obtain a second processed target mass spectrum data .” “ Performing a spectrum conversion step, wherein the second processed target mass spectrum data is processed by a mass-to-charge ratio conversing method so as to obtain a converted mass spectrum data .” “ Performing a binning step, wherein a data interval value of the converted mass spectrum data is adjusted so as to obtain the normalized target mass spectrum data .” Claim 8: “ Performing a reference calibration step, wherein a background noise of each of the reference mass spectrum data is removed so as to obtain a plurality of first processed reference mass spectrum data .” “P erforming a reference sampling normalization step, wherein a temporal resolution value of each of the first processed reference mass spectrum data is adjusted so as to obtain a plurality of second processed reference mass spectrum data .” “P erforming a reference spectrum conversion step, wherein each of the second processed reference mass spectrum data is processed by a mass-to-charge ratio conversing method so as to obtain a plurality of converted reference mass spectrum data .” “P erforming a reference binning step, wherein a reference data interval value of each of the converted reference mass spectrum data is adjusted so as to obtain the normalized reference mass spectrum data. ” Claim 11: “A model establishing step, and the model establishing step comprises: …” “P erforming a reference spectrum pre-processing step, wherein the reference mass spectrum data are pre-processed so as to obtain a plurality of normalized reference mass spectrum data .” “P erforming a model training step, wherein the normalized reference mass spectrum data are trained to achieve a convergence by an algorithm classifier so as to obtain the antibiotic resistance assessing classifier .” Because these claim limitations are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof. If applicant intends to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may : ( 1) amend the claim limitations to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitations do not recite sufficient structure, materials, or acts to perform the claimed function. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the appl icant regards as his invention. Claim s 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Regarding claim 1 (and dependent claims 2-10) : Claim limitations “ the reference mass spectrum data are obtained by processing a processed reference sample with a conventional sample processing method or a rapid sample processing method ” and “ performing a sample pre-processing step, wherein the test sample is processed by the conventional sample processing method or the rapid sample processing method so as to obtain a processed sample ” make the claim indefinite because the scope of the conventional sampling processing method and the rapid sample processing method cannot be determined. Due to the unclear descriptions in the specification and explicit statements that the conventional and rapid sample processing methods are not limited to the disclosure it is not possible to determine what constitutes a conventional or rapid sample processing method . For the purposes of examination, conventional and rapid sample processing methods will be interpreted as any possible method that prepares microorganisms for mass spectrometry experiments on any kind of mass spectrometer. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claims 2-10 are also rejected because they depend from claim 1 and do not resolve the issue of indefiniteness present in claim 1 . Regarding claim 11 (and dependent claims 12 and 13) : The claim is rejected for the same issues of indefiniteness in claim 1 where claim 11 recites a conventional or rapid sample processing method . Furthermore, t he term “ signally ” in claim 11 is a subjective term which renders the claim indefinite. Merriam-Webster dictionary defines signally as: in a signal manner (i.e., notably). The term “ signally ” is not defined by the claim, the specification does not provide a n objective standard for ascertaining the scope of the term as to when the connections between components in the claim are notable , and one of ordinary skill in the art would need to use their subjective judgment and opinion to determine the scope of the invention (See MPEP 2173.05(b)(IV)). For the purposes of examination, when claim elements are recited as signally connected it will be interpreted as data being able to be transferred between the elements. Additionally, the claim recites the limitation, “wherein the antibiotic resistance assessing classifier is established by a model establishing step, and the model establishing step comprises… so as to obtain the antibiotic resistance assessing classifier.” It is unclear if the metes and bounds of the claimed invention is intended for the processor to perform the steps to establish the antibiotic resistance classifier or if the claim merely recited the process by which the classifier was previously established outside the metes and bounds of the invention as a product-by-process limitation. This creates confusion as to when direct infringement occurs. Namely, when someone creates an antibiotic resistance classifier by the claimed model establishing step or uses an antibiotic resistance classifier in the claimed system. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claim s 12-13 are also rejected because they depend from claim 11 and do not resolve the issue of indefiniteness present in claim 11. The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claim s 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Regarding claim 1 (and dependent claims 2-10) : The limitations of “performing a model training step, wherein the normalized reference mass spectrum data are trained to achieve a convergence by an algorithm classifier so as to obtain an antibiotic resistance assessing classifier” and “performing a feature extraction step, wherein the normalized target mass spectrum data is trained to achieve a convergence by the antibiotic resistance assessing classifier so as to obtain a spectrum feature” are the subject matter not properly described in the application as filed because data is not trained in machine learning applications . A review of machine learning applications to antimicrobial resistance by Anahtar et al. teaches “ Applications of supervised machine learning to data from platform technologies such as high-throughput fluorescence microscopy and mass spectrometry-based metabolomics are now enabling the inference of antimicrobial mechanisms of action based on morphology or biochemical fingerprint. These techniques work by first characterizing the cellular responses to well-characterized antimicrobials and then applying machine learning algorithms to these measurements to train an antimicrobial classifier ” ( Understanding antimicrobial mechanisms of action , paragraph 2 ). Furthermore, Anahtar e t al. teaches that “ Most ML models train on continuous or binary vectors of fixed width. Transforming complex input data into this form is known as ‘ feature extraction ’” and “After featurization, the prediction of antimicrobial resistance becomes a routine supervised learning problem” (p. 6, General strategies for prediction, paragraphs 1-2). This shows that feature extraction is done before any machine learning model is trained on the data ( p. 5, Figure 2B). Claims 2-10 are also rejected because they depend from claim 1 and do not resolve the issue of the written description requirement. Regarding claim 6: T he limitation of “wherein the drug-resistant microorganism is Carbapenem-resistant Escherichia cloacae (CRECL) ” is the subject matter not properly described in the application as filed because Escherichia cloacae is not a known bacterial species in the art. A recent review of carbapenem resistance in Enterobacterales does not teach the existence of CRECL ( Caliskan-Aydogan et al., Microorganisms 2023, 11, 1491 ; Section two) . Additionally, phylogenetic analysis of different Escherichia isolates did not show the existence of CRECL (Walk et al., Applied a nd Environmental Microbiology, 2009, p. 6534–6544 ). However, it may be the case the claim limitation is referring to carbapenem-resistance Enterobacter cloacae which is known in the art. In such a case it is recommended to amend the claim to overcome the rejection. Regarding claim 11 (and dependent claims 12 and 13) : The same issues of claim 1 apply where claim 11 recites “wherein the normalized target mass spectrum data is trained to achieve a convergence by the antibiotic resistance assessing classifier so as to obtain a spectrum feature” and “wherein the normalized reference mass spectrum data are trained to achieve a convergence by an algorithm classifier so as to obtain the antibiotic resistance assessing classifier.” T he limitation of “wherein the processor comprises a drug-resistant microorganism assessing program…” is the subject matter not properly described in the application as filed because a processor, as widely understood, can have machine code stored in non-volatile, read-only memory to manage internal operations, but does not hold user software. Rather, a computer requires memory to hold programs and data and a processor to execute those programs . Furber reviews the history and development of microprocessors ( Proc. R. Soc. 2017. A473: 20160893 ) . Claims 12 and 13 are also rejected because they depend upon claim 11 and do not resolve the issue of the written description requirement. 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 1-1 3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Step 1: The first part of the eligibility analysis evaluates whe ther a claim fall s withing any statutory category (See MPEP 2106.03). C laims 1-10 recite a series of steps to take to train a machine learning model to predict whether a sample collected from a patient contains a drug-resistant microorganism by analyzing its’ mass spectrum. The claims are directed to a method and fall within one of the statutory categories of invention ( Step 1: YES ). Claims 11-13 recite a non-transitory machine readable medium and a processor which implement a drug-resistant microorganism assessing program. The claims are directed to a computer system, which is a machine, and falls within one of the statutory categories of invention ( Step 1: YES ). Step 2A, prong 1: In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea: Claim 1 (and its’ dependent claims 2-10) recites performing a reference spectrum pre-processing step, wherein the reference mass spectrum data are pre-processed so as to obtain a plurality of normalized reference mass spectrum data, performing a model training step, wherein the normalized reference mass spectrum data are trained to achieve a convergence by an algorithm classifier so as to obtain an antibiotic resistance assessing classifier, performing a spectrum pre-processing step, wherein the target mass spectrum data is pre-processed so as to obtain a normalized target mass spectrum data, performing a feature extraction step, wherein the normalized target mass spectrum data is trained to achieve a convergence by the antibiotic resistance assessing classifier so as to obtain a spectrum feature, and performing an assessing step, wherein the spectrum feature is analyzed by the antibiotic resistance assessing classifier so as to output an assessed result of drug-resistant microorganism, and the assessed result of drug-resistant microorganism is for assessing whether the test microorganism is a drug-resistant microorganism or not. Claim 3 recites wherein the spectrum pre-processing step comprises: performing a calibration step, wherein a background noise of the target mass spectrum data is removed so as to obtain a first processed target mass spectrum data; performing a sampling normalization step, wherein a temporal resolution value of the first processed target mass spectrum data is adjusted so as to obtain a second processed target mass spectrum data; performing a spectrum conversion step, wherein the second processed target mass spectrum data is processed by a mass-to-charge ratio conversing method so as to obtain a converted mass spectrum data; and performing a binning step, wherein a data interval value of the converted mass spectrum data is adjusted so as to obtain the normalized target mass spectrum data. Claim 4 recites wherein a mass-to-charge ratio of the normalized target mass spectrum data ranges from 2,000 to 14,000 Daltons . Claim 5 recites wherein the mass-to-charge ratio of the normalized target mass spectrum data ranges from 4,000 to 12,000 Daltons . Claim 6 recites wherein the drug-resistant microorganism is Methicillin-resistant Staphylococcus aureus (MRSA), Vancomycin-resistant Enterococci (VRE), Carbapenem- resistant Acinetobacter baumannii (CRAB), Carbapenem- resistant Pseudomonas aeruginosa (CRPA), Carbapenem-resistant Klebsiella pneumoniae (CRKP), Carbapenem- resistant Escherichia coli (CREC), Carbapenem-resistant Escherichia cloacae (CRECL), or Carbapenem-resistant Morganella morganii (CRMM). Claim 8 recites wherein the reference spectrum pre-processing step comprises: performing a reference calibration step, wherein a background noise of each of the reference mass spectrum data is removed so as to obtain a plurality of first processed reference mass spectrum data; performing a reference sampling normalization step, wherein a temporal resolution value of each of the first processed reference mass spectrum data is adjusted so as to obtain a plurality of second processed reference mass spectrum data; performing a reference spectrum conversion step, wherein each of the second processed reference mass spectrum data is processed by a mass-to-charge ratio conversing method so as to obtain a plurality of converted reference mass spectrum data; and performing a reference binning step, wherein a reference data interval value of each of the converted reference mass spectrum data is adjusted so as to obtain the normalized reference mass spectrum data. Claim 9 recites wherein the algorithm classifier is a boosting algorithm classifier. Claim 10 recites wherein a mass-to-charge ratio of each of the normalized reference mass spectrum data ranges from 2,000 to 14,000 Daltons . Claim 11 (and its’ dependent claims 12 and 13) recites a spectrum pre-processing module for pre-processing the target mass spectrum data so as to obtain a normalized target mass spectrum data , a calibration unit for removing a background noise of the target mass spectrum data so as to obtain a first processed target mass spectrum data , a sampling normalization unit for adjusting a temporal resolution value of the first processed target mass spectrum data so as to obtain a second processed target mass spectrum data , a spectrum conversion unit for processing the second processed target mass spectrum data by a mass-to-charge ratio conversing method so as to obtain a converted mass spectrum data, a nd then a data interval value of the converted mass spectrum data is adjusted so as to obtain the normalized target mass spectrum data, an antibiotic resistance assessing classifier wherein, the normalized target mass spectrum data is trained to achieve a convergence so as to obtain a spectrum feature, and the spectrum feature is analyzed so as to output an assessed result of drug-resistant microorganism , performing a reference spectrum pre-processing step, wherein the reference mass spectrum data are pre-processed so as to obtain a plurality of normalized reference mass spectrum data , performing a model training step, and wherein the normalized reference mass spectrum data are trained to achieve a convergence so as to obtain the antibiotic resistance assessing classifier . Claim 13 recites wherein a mass-to-charge ratio of the normalized target mass spectrum data ranges from 2,000 to 14,000 Daltons. The limitations of pre-processing target or reference mass spectrum data, removing background noise, adjusting temporal resolution values, mass-to-charge ratio conversion, and adjusting a data interval in claims 1, 3, 8 , and 11 are verbal equivalents that describe mathematical calculations necessary to normalize raw mass spectra. The limitation of using normalized reference mass spectra to train an algorithm classifier by reaching convergence is another verbal recitation of mathematical calculations necessary to train a machine-learning model. The limitation of normalized target mass spectrum data is trained to achieve a convergence so as to obtain a spectrum feature is a verbal recitation of mathematical calculations used to identify a relevant spectrum feature. The remaining limitation of spectrum feature analysis is a generic recitation of data analysis that can be practically performed in the human mind because a person is capable of identifying relevant information, comparing values, and determining information . Merely reciting that a mental process or mathematical calculations are being performed in a generic computer does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. Claims 4 , 5 , and 13 only provide a description of the resulting m/z range of the normalized target mass spectrum after performing the necessary mathematical calculations. Claim 10 does the same for the normalized reference mass spectra. Similarly, claim 9 only describes the type of algorithm classifier that is being trained by mathematical calculations in claim 1. Claim 6 recites naturally occurring drug-resistant bacterial species. Therefore, these limitations fall under the “Mathematical concepts , ” “Mental processes , ” and “Products of Nature” groupings of abstract ideas ( Step 2A, prong 1: YES ). Step 2A, prong 2: Claims found to recite a judicial exception under Step 2A, prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application (Step 2A, prong 2). The claims recite the following additional elements: Claim 1 (and its’ dependent claims 2-10) recites providing a drug-resistance database, wherein the drug-resistance database comprises a plurality of reference mass spectrum data, and the reference mass spectrum data are obtained by processing a processed reference sample with a conventional sample processing method or a rapid sample processing method , providing a test sample, wherein the test sample comprises a test microorganism , performing a sample pre-processing step, wherein the test sample is processed by the conventional sample processing method or the rapid sample processing method so as to obtain a processed sample , and performing an analysis step, wherein the processed sample is detected by a mass spectrometry method so as to obtain a target mass spectrum data . Claim 2 recites wherein the test sample is processed by a step-by-step centrifuging method in the rapid sample processing method, and the step-by-step centrifuging method comprises: performing a centrifuging step, wherein the test sample is processed by a plurality of centrifugations so as to obtain a centrifuged sample, and the centrifuged sample comprises the test microorganism , performing a reactive step, wherein a reaction reagent is added to the centrifuged sample and then well mixed so as to obtain a post-reaction sample , and performing a final centrifuging step, wherein the post-reaction sample is centrifuged so as to obtain the processed sample; wherein the reaction reagent comprises thioglycolate broth, ethanol, formic acid or acetonitrile. Claim 7 recites wherein the mass spectrometry method is MALDI-TOF (matrix assisted laser desorption ionization time-of-flight) method. Claim 11 recites a non-transitory machine readable medium for storing a target mass spectrum data, wherein the target mass spectrum data is obtained by detecting a processed sample by a mass spectrometry method, the processed sample comprises a test microorganism, and the processed sample is obtained by a conventional sample processing method or a rapid sample processing method, a processor signally connected to the non-transitory machine readable medium, and the model establishing step comprises: providing a drug-resistance database, wherein the drug-resistance database comprises a plurality of reference mass spectrum data, and the reference mass spectrum data are obtained by processing a processed reference sample with a conventional sample processing method or a rapid sample processing method. Claim 12 recites wherein the mass spectrometry method is MALDI-TOF. The additional elements in claim 1 of how the target mass spectrum data are obtained and providing a drug-resistance database are nominal or tangential additions that are considered pre-solution activity (e.g., a step of gathering data for use in a claimed process) because there are no structural limitations that amount to significant limits on the claim and they only select a particular data source for the mass spectra. The limitations reciting the additional elements are mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity. See MPEP 2106.05(g). The additional elements in claim 2 describing the centrifuging and chemistry steps for processing a test sample via a rapid sample processing method are merely describing the nominal or tangential additions of the sample pre-processing step recited in claim 1. Likewise, the additional element in claim 7 is only describing the type of mass spectrometry method used in the pre-solution activity step of performing an analysis step in claim 1. The additional element of a processor and a drug-resistant microorganism assessing program (with all its’ subcomponents) in claim 11 do not have any limitations that indicate that it requires anything other than carrying out the recited mental process or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. Similarly, the additional element of a non-transitory machine readable medium does not have any limitations that indicate it does anything other than its ordinary capacity of storing data and amounts to an invocation of a computer as a tool to perform an existing process. The limitations provide nothing more than mere instructions to apply the judicial exception in a generic computer environment (See MPEP 2106.05f). The remaining additional elements in claim 11 of how the target mass spectrum data are obtained and providing a drug-resistance database are nominal or tangential additions that are considered pre-solution activity (e.g., a step of gathering data for use in a claimed process) because there are no structural limitations that amount to significant limits on the claim and they only select a particular data source for the mass spectra. The limitations reciting the additional elements are mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity. See MPEP 2106.05(g). The additional element in claim 12 is merely describing the nominal or tangential addition of the type of mass spectrometry method in the pre-solution activity of obtaining mass spectra in claim 11. Therefore, the judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies/uses the recited judicial exception in some other meaningful way and the claims are directed to the judicial exception ( Step 2A, prong 2: NO ). Step 2B: Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims recite additional elements that equate to mere instructions to apply the recited judicial exception in a generic computing environment. Claims that amount to nothing more than instructions to apply the judicial exception using a generic computer do not render an abstract idea eligible. Alice Corp ., 576 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements recited in the claims amount to well-understood, routine and conventional (WURC) activity because paragraph 0024 of the specification states “In detail, in the current clinical procedures for diagnosing bacterial infections, the sample of the patient are cultured first so as to obtain microorganisms therein for species identification, and then the microorganisms are taken for incremental culture for antibiotic susceptibility testing or mass spectrometry analysis” and paragraph 0025 states MALDI-TOF mass spectrometry is the current routine clinical diagnostic method for species identification. The claims also recite computer functions that the courts have ruled to be WURC such as, storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. The claims also recite computer functions that the courts have ruled to be WURC such as, receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE , Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). As such, the combination of additional elements recited in the claims is well-understood, routine and conventional. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transform the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B: NO ) and claims 1-13 are not patent eligible. Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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. Claims 1, 6 -7, and 9 -10 are rejected under 35 U.S.C. 102 (a)(1) as being clearly anticipated by Weis et al. ( bioRxiv preprint. 2020) . The italicized text corresponds to the instant claim limitations. Weis et al. teaches training a machine-learning model on species specific MALDI-TOF mass spectra to predict drug resistance in pathogens at a much fast turnaround time than conventional methods (Abstract). Regarding claim 1 : Weis et al. teaches “As a foundation, we have curated the Database of Resistance against Antimicrobials with MALDI-TOF Mass Spectrometry (DRIAMS): an unparalleled, routinely-acquired number of bacterial and fungal MALDI-TOF mass spectra and corresponding antimicrobial resistance profiles…” (p. 2, Main section, paragraph 3; providing a drug-resistance database, wherein the drug-resistance database comprises a plurality of reference mass spectrum data, and the reference mass spectrum are obtained by processing a processed reference sample with a conventional sample processing method or a rapid sample processing method ). Weis et al. describes the mass spectra preprocessing steps done for all machine-learning analysis in their study (p. 18, Spectral representation section; performing a reference spectrum pre-processing step, wherein the reference mass spectrum data are pre-processed so as to obtain a plurality of normalized reference mass spectrum data and performing a spectrum pre-processing step, wherein the target mass spectrum data is pre-processed so as to obtain a normalized target mass spectrum data ). Weis et al. teaches training several state-of-the-art classification algorithms on their mass spectra data (p. 19, Machine learning methods section; performing a model training step, wherein the normalized reference mass spectrum data are trained to achieve a convergence by an algorithm classifier so as to obtain an antibiotic resistance assessing classifier ). Weis et al. shows their workflow includes collecting test samples , culturing (i.e., processing) them, and acquiring mass spectra from them (p. 4 , Figure 1A( i ) ; providing a test sample, wherein the test sample comprises a test microorganisms, performing a sample pre-processing step, wherein the test sample is processed by the conventional sample processing method or the rapid sample processing method so as to obtain a processed sample, and performing an analysis step, wherein the processed sample is detected by a mass spectrometry method so as to obtain a target mass spectrum data ). Lastly, Weis et al. discloses creating fixed-length feature vectors for each sample and making predictions using their machine-learning model (p. 3, Machine learning for MALDI-TOF MS based antimicrobial susceptibility prediction section, paragraph 1; performing a feature extraction step, wherein the normalized target mass spectrum data is trained to achieve a convergence by the antibiotic resistance assessing classifier so as to obtain a spectrum feature and performing an assessing step, wherein the spectrum feature is analyzed by the antibiotic resistance assessing classifier so as to output an assessed result of drug-resistant microorganism, and the assessed result of drug-resistant microorganism is for assessing whether the test microorganism is a drug-resistant microorganism or not ). Regarding claim 6: Weis et al. teaches in supplemental table 2 that their machine-learning model identified several specific drug-resistant species including: methicillin-resistant Staphylococcus aureus (because Weis et al. teaches , oxacillin resistance detects methicillin-resistant S. aureus (MRSA) strains ; p. 6, Species-specific AMR prediction yield high performance for clinically-relevant pathogens, paragraph 1 ) , vancomycin- resistant Enterococci (i.e., Enterococcus faecium and Enterococcus faecalis ), carbapenem-resistant Pseudomonas aeruginosa (shown as resistant to imipenem and meropenem which are types of carbapenem antibiotics), carbapenem-resistant Klebsiella pneumoniae , and carbapenem-resistant Escherichia coli . (p. 26-28; wherein the drug-resistant microorganism is Methicillin-resistant Staphylococcus aureus (MRSA), Vancomycin-resistant Enterococci (VRE), Carbapenem-resistant Acinetobacter baumannii (CRAB), Carbapenem-resistant Pseudomonas aeruginosa (CRPA), Carbapenem-resistant Klebsiella pneumoniae (CRKP), Carbapenem-resistant Escherichia coli (CREC), Carbapenem-resistant Escherichia cloacae (CRECL), or Carbapenem-resistant Morganella morganii (CRMM)) . Regarding claim 7: Weis et al. teaches that they use MALDI-TOF mass spectrometry to obtain their data (p. 3, DRIAMS: Clinical routine database combining MALDI-TOF mass spectra and antimicrobial resistance profiles section; wherein the mass spectrometry method is MALDI-TOF (matrix assisted laser desorption ionization time-of-flight) method ). Regarding claim 9: Weis et al teaches that they used gradient-boosted decision trees ( LightGBM ) in one of their machine-learning models ( p. 3, Machine learning for MALDI-TOF MS based antimicrobial susceptibility prediction section, paragraph 1; wherein the algorithm classifier is a boosting algorithm classifier ). Regarding claim 10: Weis et al. teaches “… the spectra were trimmed to values in a 2,000 to 20,000 Da range.” (p. 18, Spectral representation section; wherein a mass-to-charge ratio of each of the normalized reference mass spectrum data ranges from 2,000 to 14,000 Daltons ). The entire claimed m/z ratio range is encompassed and taught. 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Weis et al. ( bioRxiv preprint. 2020) as applied to claims 1, 6-7, and 9-10 above, and further in view of Ponderand et al. (Annals of Clinical Microbiology and Antimicrobials, vol. 19, no. 1, 2020, p. 60 ; 6/27/2022 IDS document ). The italicized text corresponds to the instant claim limitations. The limitations of claim s 1, 6-7, and 9-10 have been taught by Weis et al. Weis et al. is silent to all the limitations of the step-by-step centrifuging method in claim 2. However, these limitations were known in the art at the effective filing date of the invention, as taught by Ponderand et al. Ponderand et al. assess the performance of a rapid version of the Sepsityper ® kit (Bruker Daltonics ) in identification of bacteria and yeast