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 1-114 are cancelled.
Claims 115-134 are newly added.
Claims 115-134 are currently pending and under exam herein.
Claims 115-134 are rejected.
Priority
Applicant’s claim for domestic benefit to the earlier filed international application PCT/US2021/059559, filed November 16, 2021, which claims the benefit of U.S. Provisional Application No. 63/114,447 filed on November 16, 2020, is acknowledged. At this point in examination, the effective filing date of claims 115-134 is November 16, 2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 12, 2025, November 20, 2025, and March 11, 2026, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Reference number 400 in paragraph 0069 of the published specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Reference character 900 in Figure 9. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 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 applicant regards as his invention.
Claim 123 is 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 123, the limitation “wherein the trained model is trained with a set of functional gene and biochemical pathway abundances that are present or absent with a characteristic abundance for a cancer of interest” renders the claim indefinite because it is unclear whether the metes and bounds of the invention are intended to require performing the training with the recited data or if the claim merely recites how the trained model was created outside the bounds of the invention as a product-by-process limitation. This creates confusion as to when direct infringement occurs.
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 115-134 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 whether a claim falls within any statutory category (MPEP 2106.03). Claims 115-134 recite steps to perform a metagenomic analysis pipeline on sequencing data and inputting the processed data into a machine learning model to determine the presence of cancer. The claims are directed to a method and fall 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 those concepts:
Claim 115 recites: (b) filtering the sequencing reads with a genome database to produce a set of filtered non-human sequencing reads; (c) decontaminating the filtered non-human sequencing reads to remove contaminant non-human sequencing reads from the filtered non-human sequencing reads; (d) translating the decontaminated non-human sequencing reads to non-human proteins; (e) mapping the non-human proteins to a protein database, thereby producing a set of protein database associations; and (f) processing the set of protein database associations with a trained model thereby determining the presence of cancer of the subject.
Claim 116 recites: wherein the set of protein database associations comprises a set of functional genes, biochemical pathways, or a combination thereof.
Claim 120 recites: wherein the subject is human or a non-human mammal.
Claim 122 recites: wherein the genome database is a human genome database.
Claim 123 recites: wherein the trained model is trained with a set of functional gene and biochemical pathway abundances that are present or absent with a characteristic abundance for a cancer of interest.
Claim 124 recites: wherein the non-human sequencing reads originate from bacterial, archaeal, fungal, viral, or any combination thereof origins of life.
Claim 125 recites: wherein the trained model is configured to determine a category or tissue-specific location of the cancer of the subject.
Claim 126 recites: wherein the trained model is configured to determine one or more types of cancer of the subject.
Claim 127 recites: wherein the trained model is configured to determine one or more subtypes of the cancer of the subject.
Claim 128 recites: wherein the trained model is configured to determine a stage of the cancer of the subject, cancer prognosis of the subject, or a combination thereof.
Claim 129 recites: wherein the trained model is configured to determine the presence of cancer at stage I or stage II.
Claim 130 recites: wherein the trained model is configured to determine an immunotherapy response of the subject when the subject is provided an immunotherapy to treat the cancer of the subject.
Claim 131 recites: providing, as an output of the trained model, a therapy for the subject to treat the subject's cancer, wherein the subject will respond with positive therapeutic efficacy when administered the therapy.
Claim 132 recites: wherein the cancer of the subject comprises: acute myeloid leukemia, adrenocortical carcinoma, bladder urothelial carcinoma, brain lower grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma, endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, esophageal carcinoma, glioblastoma multiforme, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma, paraganglioma, prostate adenocarcinoma, rectum adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, testicular germ cell tumors, thymoma, thyroid carcinoma, uterine carcinosarcoma, uterine corpus endometrial carcinoma, uveal melanoma, or any combination thereof.
Claim 134 recites: wherein the trained model comprises one or more machine learning algorithms.
The limitations recited in claim 115 of filtering sequencing reads with a genome database, mapping the non-human proteins to a protein database, and processing the set of protein database associations with a trained model encompass mathematical calculations performed in algorithms to group sequencing reads into taxonomic clades, mathematical calculations performed in algorithms such as gene set enrichment to associate proteins with certain properties/characteristics, and mathematical calculations in machine learning algorithms to output the prediction of cancer from the data, respectively. The limitations of decontaminating sequencing reads and translating the reads into proteins respectively encompass the mental processes of observing and evaluating sequence data to remove undesired reads and determining which proteins are encoded by the reads.
The limitation in claim 131 encompasses the mental process of evaluating data in the trained model and making a judgment on which therapy will have a therapeutic affect to a subject with cancer.
The limitation recited in claim 116 only further limits the mathematical calculations in claim 115 of mapping proteins to obtain the particular associations of functional genes and/or biochemical pathways.
The limitation recited in claim 120 further limits the mathematical calculations in claim 115 of processing reads with the trained model to output a determination of cancer in either a human or other mammal. Likewise, the limitations recited in claims 123, 125-130, 132, and 134 further limit the processing of reads with the trained model by specifying the data types the model is trained on, the category and location of the cancer the model can determine, how many types of cancer the model can determine, the ability of the model to determine subtypes of cancer, the stage of cancer or cancer prognosis the model can determine, the ability of the model to determine an immunotherapy response, and limiting the type of mathematical calculation the model implements.
The limitation recited in claim 122 further limits the mathematical calculations in claim 115 of filtering sequence reads using a particular database. Likewise, the limitation recited in claim 124 further limits the filtering sequence reads to produce non-human reads originating from the claimed groups of organisms.
Therefore, these limitations fall under the “Mathematical concepts” and “Mental processes” 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 115 recites: (a) providing one or more sequencing reads of a subject's biological sample.
Claim 117 recites: wherein translating is completed in silico.
Claim 118 recites: wherein the biological sample is a tissue, liquid biopsy, or a combination thereof.
Claim 119 recites: wherein the liquid biopsy comprises: plasma, serum, whole blood, urine, cerebral spinal fluid, saliva, sweat, tears, exhaled breath condensate, or any combination thereof.
Claim 121 recites: wherein the biological sample comprises a nucleic acid composition, wherein the nucleic acid composition comprises DNA, RNA, cell-free DNA, cell- free RNA, exosomal DNA, exosomal RNA, or any combination thereof.
Claim 133 recites: wherein filtering comprises computationally filtering of the sequencing reads by bowtie2, Kraken, or a combination thereof programs.
The additional element recited in claim 115 of providing sequencing reads of a subject’s biological sample is broadly recited and amounts to a data gathering step required to use the recited judicial exception which is insignificant extra-solution activity (MPEP 2106.05(g)).
The additional element recited in claim 117 of performing the translation of sequence reads into proteins in silico and the additional elements recited in claim 133 of computationally filtering of the reads by bowtie2, Kraken, or a combination thereof are mere instruction to apply the judicial exception in a generic computer environment by invoking computers to perform the process of translation and filtering; respectively (MPEP 2106.05(f)).
The additional elements recited in claims 118-119 and 121 further limit the extra-solution activity in claim 115 of providing sequencing reads by specifying particular sources of the data.
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 additional elements recited in claims 117 and 133 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 in claims 115, 118-119, and 121 also encompass computer functions that the courts have ruled to be well-understood, routine, and conventional (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); 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). The additional elements in claim 133 are also WURC because several review articles cite using the recited bioinformatics programs: Jo et al. (Journal of Microbiology, vol. 58, no. 3, pp. 176-92; p. 181-182, Software), Oliva et al. (Bioinformatics, vol. 36, no. 16, pp. 4399-405; p. 4400, 2.4 Read alignment, paragraph 1), and Zhou et al. (Molecular Ecology, vol. 23, no. 7, pp. 1679-700; p. 1685-1689, Sequence reads of low quality, paragraphs 3-4).
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 115-134 are not patent eligible.
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.
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.
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 115-132 and 134 are rejected under 35 U.S.C. 103 as being unpatentable over Poore et al. (WO2020093040A1; IDS document 8/12/2025) in view of Apte et al. (US10787714B2; IDS document 8/12/2025). The italicized text corresponds to the instant claim limitations.
Regarding claim 115, Poore et al. teach methods of diagnosing cancer in a human patient from sequence data obtained from a sample (paragraphs 0007, 0030) which discloses a method of determining the presence of cancer of a subject, the method comprising:(a) providing one or more sequencing reads of a subject's biological sample. Poore et al. further teach removing contamination from the data features (paragraph 0027) which discloses (c) decontaminating the filtered non-human sequencing reads to remove contaminant non- human sequencing reads from the filtered non-human sequencing reads.
Regarding claims 118-119, Poore et al. teach the sample from the patient can be tissue or blood-derived tissue (paragraphs 0010-0012, 0035) which discloses the claim 118 limitations of wherein the biological sample is a tissue, liquid biopsy, or a combination thereof and the claim 119 limitations of wherein the liquid biopsy comprises: plasma, serum, whole blood…
Regarding claim 120, Poore et al. teach the subject can be a human or more broadly another mammal (paragraphs 0007-0010) which discloses wherein the subject is human or a non-human mammal.
Regarding claim 121, Poore et al. teach the sample may comprise sequence data from various sources of DNA and/or RNA including cell-free and exosomal nucleic acids (paragraph 0030) which discloses wherein the biological sample comprises a nucleic acid composition, wherein the nucleic acid composition comprises DNA, RNA, cell-free DNA, cell- free RNA, exosomal DNA, exosomal RNA, or any combination thereof.
Regarding claim 124, Poore et al. teach the non-mammalian sequence features originate from microbes in the viral, bacterial, archaeal, and/or fungal domain of life (paragraph 0028) which discloses wherein the non-human sequencing reads originate from bacterial, archaeal, fungal, viral, or any combination thereof origins of life.
Regarding claims 125-127 and 134, Poore et al teach their invention utilizes machine learning architecture and predicts the cancer type, subtype, and the location of the cancer (paragraphs 0007-0008, 0013, 0024-0025) which discloses the claim 125 limitations of wherein the trained model is configured to determine a category or tissue-specific location of the cancer of the subject, the claim 126 limitations of wherein the trained model is configured to determine one or more types of cancer of the subject, the claim 127 limitations of wherein the trained model is configured to determine one or more subtypes of the cancer of the subject and the claim 134 limitations of wherein the trained model comprises one or more machine learning algorithms.
Regarding claims 128-130, Poore et al. teach determining the stage of cancer (including stage I and II) cancer prognosis, and predicting whether a subject will response to a particular treatment (paragraphs 0015-0016, 0018, 0071) which discloses the claim 128 limitations of wherein the trained model is configured to determine a stage of the cancer of the subject, cancer prognosis of the subject, or a combination thereof, the claim 129 limitations of wherein the trained model is configured to determine the presence of cancer at stage I or stage II, and the claim 130 limitations of wherein the trained model is configured to determine an immunotherapy response of the subject when the subject is provided an immunotherapy to treat the cancer of the subject.
Regarding claim 131, Poore et al. teach predicting if a subject will response to a particular treatment and treating the subject based on the sequence features (paragraphs 0018-0019, 0070-0072) which discloses providing, as an output of the trained model, a therapy for the subject to treat the subject's cancer, wherein the subject will respond with positive therapeutic efficacy when administered the therapy.
Regarding claim 132, Poore et al. teach their method diagnoses acute myelogenous leukemia, adrenocortical cancer, bladder cancer, brain lower grade glioma, breast cancer, cervical cancer, cholangiocarcinoma, colon cancer, esophageal cancer, brain glioblastoma, head and neck cancer, kidney chromophobe, kidney renal clear cell carcinoma, kidney papillary cell carcinoma, liver cancer, lung adenocarcinoma, lung squamous cell carcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, ovarian cancer, prostate cancer, rectum cancer, sarcoma, skin cutaneous melanoma, stomach cancer, thymoma, thyroid carcinoma, uterine cancer, and uveal melanoma (paragraph 0014) which discloses wherein the cancer of the subject comprises: acute myeloid leukemia, adrenocortical carcinoma, bladder urothelial carcinoma, brain lower grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma, … cholangiocarcinoma, colon adenocarcinoma, esophageal carcinoma, glioblastoma multiforme, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, … ovarian serous cystadenocarcinoma, … prostate adenocarcinoma, rectum adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, … thymoma, thyroid carcinoma, uterine carcinosarcoma, … uveal melanoma.
Poore et al. teach that the microbial nucleic acids can be detected simultaneously with nucleic acids from the host and subsequently distinguished (paragraph 0033), but appear to be silent on the claim 115 limitation of (b) filtering the sequencing reads with a genome database to produce a set of filtered non-human sequencing reads. Furthermore, Poore et al. teach training machine learning model with abundance information and using trained models to diagnose/prognose cancer (paragraphs 0024-0026, 0072), but appear to be silent on the claim 115 limitations of (d) translating the decontaminated non-human sequencing reads to non-human proteins; (e) mapping the non-human proteins to a protein database, thereby producing a set of protein database associations; and (f) processing the set of protein database associations with a trained model thereby determining the presence of cancer of the subject.
Lastly, Poore et al. appear to also be silent on the limitations of claims 116-117 and 122-123. However, these limitations were known in the prior art before the effective filing date of the invention as taught by Apte et al.
Regarding claim 115, Apte et al. teach removing subject genome-derived sequence by processing sequences against a reference genome such as those provided by the Genome Reference Consortium (p. 16, col. 10, lines 23-28) which discloses (b) filtering the sequencing reads with a genome database to produce a set of filtered non-human sequencing reads. Apte et al. further teach in generating a microbiome functional diversity dataset, candidate features associated with functional aspects of microbiome components are extracted by associating functional features with several gene products such as prokaryotic clusters of orthologous groups of proteins and eukaryotic clusters of orthologous groups of proteins, etc. (p. 16, col. 10, line 47 – p. 17, col. 11, line 14) which discloses (d) translating the decontaminated non-human sequencing reads to non-human proteins because identifying nucleic acid sequences with functional proteins inherently requires translating the nucleotide sequences into amino acid sequences in order to determine what proteins are encoded by the nucleotide sequences.
In further regard to claim 115, Apte et al. teach extracting candidate features can include identifying functional feature associations by searching one or more databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) (p. 17, col. 11, line 15 – col. 12, line 6) which discloses (e) mapping the non-human proteins to a protein database, thereby producing a set of protein database associations. Apte et al. go on to teach creating a characterization model and using it as a diagnostic tool by inputting data about a microbial composition and/or functional features (p. 20, col. 17, lines 40-67). The characterization model can use computational methods including machine learning and statistical methods (Id.). These teachings disclose (f) processing the set of protein database associations with a trained model thereby determining the presence of cancer of the subject.
Regarding claim 116, Apte et al. teach extracting candidate functional features such as systems information like pathway maps and functional units of genes, etc. by performing searches of databases of this kind of information such as KEGG, Clusters of Orthologous Groups (COGs), and Gene Ontology (GO) (p. 17, col. 11, line 15 – col. 12, line 6) which discloses wherein the set of protein database associations comprises a set of functional genes, biochemical pathways, or a combination thereof.
Regarding claim 117, Apte et al. teach their method can be implemented on a computing system (p. 15, col. 7, line 56 – col. 8, line 4) and using several bioinformatics tools to generate features derived from compositional and/or functional aspects of the microbiome in a biological sample by performing sequence analysis operations such as BWT-indexing with Bowtie and mapping taxa in relation to databases (p. 16, col. 10, lines 12-46) which discloses wherein translating is completed in silico.
Regarding claim 122, Apte et al. teach the subject can be human (p. 14, col. 5, line 54 – col. 6, line 32) and removing the subject’s genome-derived sequence read by mapping sequence data to a subject reference genome (p. 16, col. 10, lines 23-28) which discloses wherein the genome database is a human genome database.
Regarding claim 123, Apte et al. teach creating a characterization model from the extracted functional features (p. 20, col. 17, lines 40-67) which discloses wherein the trained model is trained with a set of functional gene and biochemical pathway abundances that are present or absent with a characteristic abundance for a cancer of interest.
An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Poore et al. teach methods to accurately diagnose and treat disease using nucleic acids of non-human origin, particularly to diagnose cancer because evidence indicates a key role for microbiota in carcinogenesis, tumor progression, and response to therapy (paragraphs 0002-0003). Poore et al. accomplish their purpose by broadly creating patterns of microbial presence or abundance ‘signatures’ that are associated with cancer (paragraph 0008). Apte et al. teach the human microbiome is suspected to play at least a partial role in many disease states and many questions in analyzing human microbiomes remain unanswered due to limitations of current approaches (p.12, col. 1, lines 45-67). The invention of Apte et al. includes methods of diagnosing patients and promoting targeted therapies to subjects suffering broadly from some physiological or psychological symptoms by analyzing their microbiome composition and functional features (p. 13, col. 3, lines 8-43). Additionally, Apte et al. teach their method can be used to characterize/diagnose seemingly unrelated condition by analyzing microbiome functional features shared across conditions in a non-obvious manner (Id.; p. 27, col. 32, lines 55-61).
Therefore, one of ordinary skill in the art would have been motivated to adapt the methods of Poore et al. with the steps in Apte et al. of extracting functional features from the nucleic acid sequence data in order to be able to identify related conditions and expand the diagnostic information obtained from processing patient samples. Furthermore, one of ordinary skill in the art would predict identification of functional features could be readily added to the methods of Poore et al. with a reasonable expectation of success because both prior art references are analyzing sequence data obtained from the microbiome of a host. The invention of claims 115-132 and 134 is therefore prima facie obvious.
Claim 133 is rejected under 35 U.S.C. 103 as being unpatentable over Poore et al. (WO2020093040A1; IDS document 8/12/2025) in view of Apte et al. (US10787714B2; IDS document 8/12/2025) as applied to claim 115 above, and further in view of Oechslin et al. (Frontiers in Cellular and Infection Microbiology, vol. 8, p. 375). The italicized text corresponds to the instant claim limitations.
The limitations of claim 115 have been taught by Poore et al. and Apte et al.
Regarding claim 113, Oechslin et al. teach using the Kraken bioinformatics program to taxonomically classify sequence reads and using the bioinformatics program Bowtie2 to align sequencing reads to their respective representative genomes (p. 5, Sequencing Analysis). Table 1 (p. 9-10) shows the results of Kraken and Bowtie2 in being able to filter out desired reads associated with the virus in the sample from the host and other microbiota (p. 6, Clinical CSF Samples). These teachings disclose wherein filtering comprises computationally filtering of the sequencing reads by bowtie2, Kraken, or a combination thereof programs.
Apte et al. teach using alignment algorithms such as Bowtie and mapping taxa to existing or custom databases (e.g., VAMPS, MG-RAST, QIIME) in their metagenomics pipeline to diagnose diseases (p. 16, col. 10, lines 12-46). The invention of Apte et al. differs from the elements of the claimed invention of using Bowtie2 and/or Kraken by the substitution of other bioinformatics algorithms to accomplish the same task. The substituted components and their functions were known in the art as taught by Oechslin et al. in performing metagenomic analysis to detect viral pathogens. One of ordinary skill in the art could have substituted the algorithms and databases used in Apte et al. for those in Oechslin et al. predictably resulting in filtering out host sequencing reads using those programs that serve the same purpose. The invention of claim 133 is therefore prima facie obvious.
Conclusion
No claims are allowed.
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/T.Y.O./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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