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- 17 are pending . Priority Acknowledgment is made of applicant's claim for foreign priority based on application 2021901584, filed in Australia on 26 May 2021 . It is noted, however, that applicant has not filed a certified copy of the application as required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statement (IDS ), filed 26 May 2022, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement wa s considered by the examiner. Drawings The drawings , filed 26 May 2022 , have been accepted by the examiner. Specification The disclosure is objected to because it contains embedded hyperlink s and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01. Hyperlinks were found within the paragraphs listed below. [0027] – “https://maq.sourceforge.net...” [0030] – “https://doi.org...” [0036] – “ https://github.com... ” (recited twice) [0049] – https://dataportal.ans ... ” 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 9 , 10 , and 17 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. Claim 9 recites “scaling up one of the control group and the group diagnosed with ALS ” wherein the claim language is unclear whether one or multiple groups are scaled . To overcome this rejection, please amend with language that indicates scaling one group “ or ” another. Claim 10 recites removing sequences with “low abundance ” wherein t he relative term " low " is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention . Please define with a more concrete metric of abundance. Claim 17 recites counting “sub-sequences that are significan t ”, wherein t he relative term " significant " is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention . Please define with a more concrete metric. 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- 17 are rejected under 35 U.S.C. 101. Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106: Claims 1- 15 are directed to a statutory category ( method ). Claim 16 is directed to a statutory category ( system ). Claim 17 is directed to a statutory category ( method ). Therefore, claims 1- 17 have patent eligible subject matter. [Eligibility Step 1: YES] Eligibility Step 2A : This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106. Eligibility Step 2A -- Prong One: The l imitations below are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon) . Claim 1: determining training sub-sequences from the multiple unaligned training reads; (mental process) counting the training sub-sequences in the control group and in the group diagnosed with ALS; (mental process, mathematical concept) determining a measure of change, for each of the training sub-sequences, in the counting between the control group and the group with ALS; (mental process, mathematical concept) selecting a subset of training sub-sequences that are distal from a mean value of the measure of change; (mental process, mathematical concept) determining testing sub-sequences from the multiple unaligned testing reads; (mental process) counting the testing sub-sequences that are in the subset; (mental process, mathematical concept) determining a diagnostic output value related to ALS for the sample based on the counting of the testing sub-sequences that are in the subset. (mathematical concept) Claim 4: The method of claim 1, wherein determining the training subsequences comprises selecting a range of base pairs from the training reads . (mental process) Claim 7: The method of claim 1, wherein counting comprises calculating a counter value for each of the training sub- sequences ; (mental process, mathematical concept) determining a measure of change comprises calculating a difference between the counter value of a sub-sequence in the control group and the counter value of the same sub-sequence in the group diagnosed with ALS. (mental process, mathematical concept) Claim 8: The method of claim 1, wherein the method further comprises normalizing the measure of change by adjusting the mean value towards zero . (mathematical concept ) Claim 9: The method of claim 8, wherein adjusting the mean value comprises scaling up one of the control group and the group diagnosed with ALS with a lower abundance in the training sequencing data. (mental process, mathematical concept) Claim 10 : The method of claim 1, wherein the method further comprises removing sub- sequences with a low abundance in the training sequencing data. (mental process) Claim 11: The method of claim 1, wherein selecting the subset comprises selecting training sub-sequences that are more than a threshold distance from the mean value. (mental process, mathematical concept) Claim 12: The method of claim 11, wherein the threshold distance is measured as a log- fold change. (mathematical concept) Claim 13: The method of claim 1, wherein determining the diagnostic output value comprises comparing the counting of the testing sub-sequences in the subset to the counting from the control group of the training sub-sequences in the subset and to the counting from the group diagnosed with ALS of the training sub-sequences in the subset. (mental process, mathematical concept) Claim 14 : The method of claim 13, wherein the method further comprises: upon determining that the counting of the testing sub-sequences in the subset is closer to the counting from the control group of the training sub-sequences in the subset than to the counting from the group diagnosed with ALS of the training sub- sequences in the subset, determining the diagnostic output value that indicates that the sample is diagnosed as not having ALS; (mental process, mathematical concept) and upon determining that the counting of the testing sub-sequences in the subset is closer to the counting from the group diagnosed with ALS of the training sub- sequences in the subset than to the counting from the control group of the training sub- sequences in the subset, determining the diagnostic output value that indicates that the sample is diagnosed as having ALS (mental process, mathematical concept) Claim 16: determining training sub-sequences from the multiple unaligned training reads; (mental process) counting the training sub-sequences in the control group and in the group diagnosed with ALS; (mental process, mathematical concept) determining a measure of change, for each of the training sub-sequences, in the counting between the control group and the group with ALS; (mental process) selecting a subset of training sub-sequences that are distal from a mean value of the measure of change; (mental process) determining testing sub-sequences from the multiple unaligned testing reads; (mental process) counting the testing sub-sequences that are in the subset; (mental process) determining a diagnostic output value related to ALS for the sample based on the counting of the testing sub-sequences that are in the subset. (mental process, mathematical concept) Claim 17 : determining testing sub-sequences from the multiple unaligned testing reads; (mental process) counting the testing sub-sequences that are in a subset of the testing sub- sequences, wherein the subset contains training sub-sequences that are significant in relation to a count of the training sub-sequences in a control group relative to a count of the training sub-sequences a group diagnosed with ALS; (mental process, mathematical concept) determining a diagnostic output value related to ALS for the sample based on the counting of the testing sub-sequences that are in the subset (mental process, mathematical concept) Determining and selecting subsequences from a larger sequence can be accomplished through making mental observation s of data, while utilizing pen and paper. As such , most of the noted limitations fall within the mental process grouping of abstract ideas. Furthermore, normalizing, scaling measurements , and c alculating delta values require both mental observation s of data and analysis techniques that can also be fully executed with use of a pen , paper , and mathematical calculations . As such, limitations that involve activities of this manner fall into the mental process and/or mathematical concept grouping of abstract ideas. As such claims 1, 4, 7-14, 16, and 17 appear to recite judicial exceptions (abstract ideas). [Eligibility Step 2A – Prong One: YES] Eligibility Step 2A – Prong Two: A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Eligibility Step 2B : Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)). The following limitations are additional elements that are analyzed to determine if they integrate the judicial exceptions into practical applications . receiving testing sequencing data comprising multiple unaligned testing reads from a sample diagnosed with ALS; (claims 1, 16, 17) receiving testing sequencing data comprising multiple unaligned testing reads from a sample to be tested for ALS; (claims 1, 16, 17) These l imitations complete necessary data gathering activities for the claimed invention and do not place necessary limits on or integrate the abstract ideas into practical application. [Eligibility Step 2A – Prong Two: YES] Such data gathering activities that assess and measure data from prior processing to be used in a diagnosis are classified as insignificant extra-solution activity . These activities are considered well-known and conventional within the art , as exemplified by Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) . [Eligibility Step 2B : NO] The following additional elements can be categorized differently: Claim 2: The method of claim 1, wherein the training reads have a length of less than 300 bases . Claim 3: The method of claim 1, wherein receiving the training sequences comprises reading a file from computer storage in FASTQ format. Claim 5 : The method of claim 4, wherein the range has a constant length for the training sub-sequences. Claim 6: The method of claim 4, wherein the range is non-overlapping between different sub-sequences. The limitations above specify the format, length, and type of analysis technique performed on the data . Selecting a particular data source or type of data to be manipulated is also classified as an insignificant extra-solution activity and does not integrate the judicial exceptions of the claimed invention as a whole into practical application per MPEP 2106.05(g) . [Eligibility Step 2A – Prong Two: YES] Considering the types of data formats ( FASTQ ) and data (non-overlapping , constant range of nucleotides) are also well known and conventional in the art , the elements further lack inventive concept, as exemplified by Intellectual Ventures I LLC v. Erie Indem . Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937 . [Eligibility Step 2B : NO] The following additional elements can be categorized differently: Claim 1: A computer-implemented method for processing sequencing data of multiple subjects, the method comprising: Claim 17: A computer-implemented method for processing sequencing data, the method comprising: Claim 16: A system for processing sequencing data of multiple subjects, the system comprising a processor configured to perform the steps of: Claim 15: A non-transitory computer-readable medium with program code stored thereon that, when executed by a computer, causes the computer to perform the method of claim 1. The limitation s above recite components of generic computing environments or implementation s of a method onto generic computer environment s . Elements of this nature do not integrate the judicial exceptions into practical application , as exemplified by Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546 . [Eligibility Step 2A – Prong Two: YES] Th ough the claimed invention is drawn to an efficient method of processing sequencing data , applications and benefits of this technique are well-known and conventional within the art , as evidenced by Ren et al . (Annu Rev Biomed Data Sci ; 2018) , which reviews alignment-free sequencing methods. [Eligibility Step 2B : NO] As such claims 1-1 7 are directed to judicial exceptions and rejected under 35 U.S.C 101 , in accordance with Alice/Mayo, MPEP 2143 evaluation. Claim Rejections - 35 USC § 103 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 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 . Claims 1 -5, 7-1 0 , and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Fisher (2017/0364666 A1) in view of Zhao (2015/0347676 A1) . Fisher describes computer implemented methods and systems of strain typing microbial samples via k- mer (subsequence) analysis . Zhao, in part, describes a method of unaligned sequence reads analysis as diagnostic tests for a range of conditions . Claims 1 and 16 are directed to computer implemented methods and systems that analyze sequence data from unaligned reads . The system obtains reads from control samples with a positive and negative amyotrophic lateral sclerosis (ALS) status. It further selects , counts , and compares their respective subsequences and computes a value represent ative of the quantity difference. The system selects subsequences with the greatest difference value as guides for determining the possible status of test sample s (subject s with unknown ALS status). Claim 15 is directed to a c omputer-readable medium with code that causes processers to perform th e method described . Claim 17 is only directed to the steps within the method that compare and quantify the difference between subsequences from test samples , ALS diagnosed samples, and negative control samples. Fisher shows a method of strain typing that does not require sequences to be aligned to a reference genome [0012]. Fisher shows generating a profile library which may include previously analyzed k- mer profiles or sequence data from relevant bioinformatics database s [0021]. Fisher shows obtaining MLST data for each MLST gene and a text profile file containing the strain type (ST) and corresponding sequence identifier for each of the genes in the profile [ 0050 ] as an exemplary profile library . Fisher further shows determining t he k- mer content for each sequence [ 0050 ] and co unting them [ f ig . 2, part 210]. Fisher further shows use of a relationship mapper that perform s pairwise comparisons of each strain of a set against each other strain of the set in order to determine a k - mer similarity score [0032] . This score serves as a metric to typ e samples [00 05 ]. Regarding claims 15, Fisher shows a computer system configured for generating a k- mer based strain type mapping which includes one or more processors, and one or more hardware storage devices having stored thereon computer-executable instructions. Fisher shows the instructions are executable by the one or more processors to cause the computer system to receive a set of nucleotide sequence data and t he nucleotide sequence data may include a plurality of nucleotide sequence data structures each corresponding to a separate microbial strain to be analyzed [000 4 ]. Regarding claim 3, Fisher teaches receiving the strain and sequence data in text-based format , inclusive of FASTQ [0021] . Regarding claims 4 and 5, Fisher teaches that e ach k- mer is a short nucleotide sequence with a length of “k” bases derived from the respective strain's sequence [0013] . As such “k” is constant for each set of subsequences. Claim s 7 and 13 are directed to counting each samples ’ subsequence s , comparing the count values, and measuring the difference. C laim 14 is drawn to identifying the ALS status of a test sample based on its subsequence quantity being more similar to the quantity of positive or negative control samples . Fisher shows k- mer profile s includ e an associated count value that indicat es the number of occurrences of the corresponding k- mer within the set [0005] . Fisher further teaches comparing k - mer profiles of unknown strains to profiles with strain type data to determine a similarity score between t he m [ 0013 ] . The similarity score may indicate a relationship mapping of the respective microbial strains corresponding to the profiles [0005] . Claim 10 is directed to removing sub-sequences below a certain quantity from the training data . Fisher shows use of a cutoff filter [ f ig . 1, part 112a ] that exclu des k- mer profiles with counts below a set frequency threshold [0024] . Fisher does not show that the training reads must be less tha n 300bp long (claim 2) or any use of the described method to ascertain the ALS status of a sample (claims 1-17) . Zhao teaches the diagnostic tests method include s processing relatively short sub-sequences from sequence reads [0014] by generating a count of subsequence reads from a test sample (count A), a count of subsequence reads from a sample with a known quantity of reads (count B), and a count representation for the difference as a ratio of the count A to the count B [0016]. Regarding claim 2, Zhao teaches the nominal, average, mean or absolute length of single-end reads is sometimes about 20 to about 30 bases [0126]. Zhao further teaches that the diagnostic testing method can be used to identify pathogen variant / strain s [0422] and elucidate disorders, including amyotrophic lateral sclerosis [0414]. It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the teachings of Zhao with the teachings of Fisher because Zhao and Fisher present analogous methods of differentiating/diagnosing samples via the analysis and comparison of unaligned read subsequences. Zhao provides sufficient teachings that the method can be applied to amyotrophic lateral sclerosis samples in the same manner it is applied to microbial strain typing. Zhao further teaches that the method can be embodied on reads less than 300bp long. Therefore, one of ordinary skill in the art can perform the method of Fisher on ALS samples with a reasonable expectation of success to the applied function. Claims 8, 9, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Fisher in view of Zhao , as applied to claims 1-5, 7-10, and 13-17 above, in further view of Erhard (Bioinformatics; Vol. 34 (23); 2018) . The claims are directed to normaliz ing the count difference towards zero (claim 8) by proportionally increasing the lower count dat a (claim 9) and selecting subsequences that are more than certain distance from a central value (claim 11) , by selecting the subsequences with the greatest log-fold change (claim 12). Fisher shows using a consensus reference generator [fig . 1, part 112c ] to select k- mer profiles according to a frequency percentage or other desired commonality metric [0028] and s electing k- mer profiles outside the consensus reference genome ( i.e. profiles with the greatest difference), to provide a greater distinction of different strains [0029]. Zhao shows that the count data can be normalized [0035], which sometimes involves scaling to adjust values [0186]. Zhao further shows calculating a statistic that provides a suitable measure of error for the count representation [0038] that is sometimes calculated as a z-score [0039] and d etermining an outcome of the diagnostic test by comparing a statistic derived from the count representation (e.g., z-score) to a predetermined threshold value for the statistic (e.g., z-score threshold) [0042] . Fisher and Zhao d o not show that the commonality metric for selecting subsequences must be represented as distance from a central value (claim 11); quantified by log-fold change deviations (claim 12) ; or normali zi ng the count difference measurement towards zero , by proportionally increasing the count data (claim 9). Erhard compares statistical frameworks for RNA-seq data analysis . Erhard teaches that when interested in the quantity difference for some biological entity between two samples A and B , t he usual representation of the extent of this difference, is the log 2 fold change, i.e. the logarithmized ratio of the quantities in A and B . lfc c a, c b = log 2 c a, c b . (page 4057 , column 1 ). Erhard further teaches that a widely used normalization procedure is to add a fixed constant to all log 2 fold changes such that their median is centered around zero ( page 4058, column 2 ) , further displayed in a normal distribution in figure 5 ( page 4059, column 2 ) . Erhard teaches that this statistical framework is particularly applicable to fragments of sequenced mRNA reads (page 4054 , column 1). As such, Fisher teaches a consensus reference generator that selects subsequences outside of a pre-determined measure. Erhard teaches that log-fold changes are a common metric when determining a count difference between two samples and can be normalized to a central zero value via proportionally increasing the data. Therefore, it is obvious to one of ordinary skill in the art to apply the specific statistical framework in Erhard to the method s of Fisher and Zhao with reasonable expectation of success. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Fisher in view of Zhao , as applied to claims 1-5, 7-10, and 13-17 previously , in further view of Bonham-Carter (Briefings in Bioinformatics. Vol. 15(6); 2013) . Claim 6 is directed to completing the method of processing unaligned sequence data via subsequence comparison and analysis, wherein the subsequences are non-overlapping. Bonham-Carter reviews methods that perform alignment-free genetic sequence comparisons using subsequences (words). Bonham-Carter teaches that one of many approach es partition a long sequence into consecutive , non-overlapping , and discrete subintervals for further analysis (page 898, column 2). Therefore, Bonham-Carter highlights that alignment-free genetic sequence comparison methods using overlapping or non-overlapping subsequences are known to those of ordinary skill in the art and thus obvious to try with the method s of Fisher and Zhao . Conclusion No claims are currently allowed. Correspondence Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milana Thompson whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-8740 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday - Friday, 9:00-6:00 ET . 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 Karlheinz Skowronek can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-1113 . 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. / M.K.T ./ Examiner, Art Unit 1687 /Karlheinz R. Skowronek/ Supervisory Patent Examiner, Art Unit 1687