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 .
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.
Claims Status
Claims 1, 3 and 6-7 are pending. Claims 1, 3 and 6 are amended. Claims 2, 4-5 and 8-11 are cancelled. Claims 1, 3 and 6-7 are examined below.
Priority
As detailed on the 07/06/2022 filing receipt, this application claims foreign priority to as early as 08/13/2019 of application IN201921032791 filed in India.
Drawings
The drawings filed 02/11/2022 are accepted.
Withdrawn Rejections/Objections
The objection to claims 1 and 2, in the Office action mailed 10/02/2025 is withdrawn in view of the amendments filed 01/12/2026. However, new objections are applied.
The rejection of claims 1-4, 6-7 and 9-11 under 35 U.S.C. §112(b), Second Paragraph, in the Office action mailed 10/02/2025 is withdrawn in view of the amendments filed 01/12/2026. However, new rejections are applied.
The rejection of claims 1-3, 6-7 and 9-11 under 35 U.S.C. §103 over Bajaj (US 2014/0179726 A1, published Jun. 26, 2014; as cited on the 02/11/2022 IDS Document) in view of Backhed (US 2015/0317444 A1, published Nov. 05, 2015; as cited on the 02/11/2022 IDS Document), Loomba ("Gut microbiome-based metagenomic signature for non-invasive detection of advanced fibrosis in human nonalcoholic fatty liver disease." Cell metabolism 25.5 (2017): 1054-1062.; as cited on the attached 892 form) and Chen ("Gene expression profiling gut microbiota in different races of humans."; as cited on the attached 892 form), in the Office action mailed 10/02/2025 is withdrawn in view of the amendments filed 01/12/2026. However, a new rejection is applied.
The rejection of claim 4 under 35 U.S.C. §103 over Bajaj (US 2014/0179726 A1, published Jun. 26, 2014; as cited on the 02/11/2022 IDS Document) in view of Backhed (US 2015/0317444 A1, published Nov. 05, 2015; as cited on the 02/11/2022 IDS Document), Loomba ("Gut microbiome-based metagenomic signature for non-invasive detection of advanced fibrosis in human nonalcoholic fatty liver disease." Cell metabolism 25.5 (2017): 1054-1062.; as cited on the attached 892 form) and Chen ("Gene expression profiling gut microbiota in different races of humans."; as cited on the attached 892 form) as applied to claims 1-3, 6-7 and 9-11 above and in further view of Stritzker (US 2008/0193373 A1, published Aug. 14, 2008; as cited on the 02/11/2022 IDS Document), in the Office action mailed 10/02/2025 is withdrawn in view of the amendments filed 01/12/2026. However, a new rejection is applied.
The rejection of claims 1-3, 6-7 and 9-11 on the ground of nonstatutory double patenting as being unpatentable over claims 11-3 and 5-11 of copending Application No. 17634634 in view of Backhed (US 2015/0317444 A1, published Nov. 05, 2015; as cited on the 02/11/2022 IDS Document) is withdrawn in view of the terminal disclaimer filed 01/12/2026 and approved 01/25/2026.
The rejection of claim 4 on the ground of nonstatutory double patenting as being unpatentable over claims 11-3 and 5-11 of copending Application No. 17634634 in view of Backhed (US 2015/0317444 A1, published Nov. 05, 2015; as cited on the 02/11/2022 IDS Document) and Stritzker (US 2008/0193373 A1, published Aug. 14, 2008; as cited on the 02/11/2022 IDS Document) is withdrawn in view of the terminal disclaimer filed 01/12/2026 and approved 01/25/2026.
The rejection of claims 1-3, 6 and 9-11 on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 7 and 9-12 of copending Application No. 17/634,949 (hereinafter referred to as App. ‘949) in view Backhed (US 2015/0317444 A1, published Nov. 05, 2015; as cited on the 02/11/2022 IDS Document) is withdrawn in view of the terminal disclaimer filed 01/12/2026 and approved 01/25/2026.
The rejection of claim 4 on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 7 and 9-12 of copending Application No. 17/634,949 (hereinafter referred to as App. ‘949) in view Backhed (US 2015/0317444 A1, published Nov. 05, 2015; as cited on the 02/11/2022 IDS Document) and Stritzker (US 2008/0193373 A1, published Aug. 14, 2008; as cited on the 02/11/2022 IDS Document) is withdrawn in view of the terminal disclaimer filed 01/12/2026 and approved 01/25/2026.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claims 1 (page 4, line 9, quantifying step) recites “…abundance of a sensory protein…,” which should be "an abundance of a sensory protein” to correct the grammar of the phrase.
Claim 1 (page 2, line 10) recites “extracting a data from the plurality of public repositories…” performing 10 replicates on 10-fold cross-validation on the training set…,” which should be “extracting data…” to correct the grammar of the phrase.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claims 1, 3 and 6-7 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 claim(s) contains 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.
Claim 1 recites “…quantifying abundance of a sensory protein from the sequenced metagenomic reads using the database of sensory protein sequences (page 4, Lines 9-11) …performing a sequence alignment with the sequences in the created sensory protein sequence database as query against the sequenced metagenomic reads (page 4, Lines 12-13) … computing cumulative matches of the sequenced metagenomic reads… (page 4, Line 17).” It is unclear how sequence alignment is performed between the protein sequences and metagenomic reads, since metagenomic reads are DNA sequences. As indicated in the specification (para. 10-11), the extracted DNA is then sequenced to get sequenced metagenomic reads. Protein sequences cannot directly align with metagenomic reads. Therefore, it is also unclear how sensory protein abundance is quantified and how the matches between the protein and metagenomic sequences are obtained.
--Dependent claims are rejected for depending on rejected claim.
Claim Rejections - 35 USC § 112/b-indefiniteness
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.
Claims 1, 3 and 6-7 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.
Claims 1 (page 2, line 24) recites “applying, via one or more hardware processors, a random forest classifier…” The “hardware processor” cannot directly recite process steps such as applying because a single claim which claims both an apparatus and the method steps of using the apparatus is indefinite (see MPEP 2173.05(p).II).
Claim 1 recites 'evaluation' models (page 3, line 19). The metes and bounds of the of the terms within the quotes are not clearly defined. It is unclear as to why the terms are within the quotes.
Claim 1 recites “…quantifying abundance of a sensory protein from the sequenced metagenomic reads using the database of sensory protein sequences (page 4, Lines 9-11) …performing a sequence alignment with the sequences in the created sensory protein sequence database as query against the sequenced metagenomic reads (page 4, Lines 12-13) … computing cumulative matches of the sequenced metagenomic reads… (page 4, Line 17).” It is unclear how sequence alignment is performed between the protein sequences and metagenomic reads, since metagenomic reads are DNA sequences. As indicated in the specification (para. 10-11), the extracted DNA is then sequenced to get sequenced metagenomic reads. Protein sequences cannot be directly aligned with metagenomic reads. Therefore, it is also unclear how sensory protein abundance is quantified and how the matches between the protein and metagenomic sequences are obtained.
Claim 1 recites “…sequences corresponding to the annotated sensory proteins are used as the database…” It is unclear what is meant by used as the database and whether this is the generation of a new database or whether it is the same database as the database in the creating step. For compact prosecution, it is interpreted as saving the data in the database created in the creating step.
Dependent claims are rejected for being dependent on rejected claim.
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, 3 and 6-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis of claims in Step 1.
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Independent claim 1 is directed to a 101 process, here a "method (400) for assessing the risk of prediabetes in a person," with process steps such as "creating…, applying…"
[Step 1: claims 1, 3 and 6-7: YES]
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:
Mental processes recited include:
Claim 1 recite: "creating a database of sensory protein sequences of a plurality of organisms…," "extracting a data from the plurality of public repositories; identifying all the annotated sensory proteins from the extracted data using a set of keyword searches; performing a sequence alignment to identify a set of poorly annotated or characterized sensory protein sequences; filtering the results of the sequence alignment based on 95% identity, 95% coverage and an e-value cut-off 1.0 *e-5 (0.00001) to identify a set of additional sensory protein sequences; and collating the sensory protein sequences and the sequences identified through sequence alignment to create the sensory protein sequence database," "... selecting a random set of sequenced metagenomic reads comprising 90% of the fecal samples as a training set and rest of the 10% were considered as a test set; performing 10 replicates on 10-fold cross-validation on the training set to build 100 cross-validation RF models; capturing a GINI index of each feature included in cross- validation models; selecting a predefined number of features based on GINI index values from each of the 100 cross-validation RF models to obtain a feature sub-set; ranking each of the features in the feature sub-set, on the basis of the sum of their GINI index values; obtaining multiple evaluation models by cumulatively adding next ranked feature in a sub-set of features with the features of the previous 'evaluation' model, wherein the first evaluation model comprised of top two features in the feature sub-set; assessing a performance of all the evaluation models on the basis of their added features; choosing the evaluation model based the assessed performance as a final classification model; evaluating the performance of the evaluation model on basis of a balancing Score, followed by Matthews correlation coefficient (MCC) and Area under the curve (AUC) scores; and validating the final classification model on the test set containing rest 10% of the dataset earlier kept aside as the independent test set, wherein accuracy of training model and a confidence probability of a binary prediction to be case or control were accounted… assessing the risk of prediabetes, wherein the microbiome sample comprising microbial cells… quantifying abundance of a sensory protein… wherein calculating the abundance of the sensory protein, comprises: performing a sequence alignment with the sequences in the created sensory protein sequence database as query against the sequenced metagenomic reads, wherein the hits satisfying a minimum e-value threshold of 0.00001 are considered as correct matches; computing cumulative matches of the sequenced metagenomic reads to form a count of sensors for each bacterial strain in the sensory protein sequence database, wherein the count of sensors indicates approximately the potential number of sensory protein coding regions in the genome for that particular bacterial strain for the microbiome sample from which the sequenced metagenomic reads were obtained; computing a cumulative length of the nucleotide bases for all these hits for each bacterial strain in the sensory protein sequence database to form a covered base length, wherein the covered base length indicates approximately a total length of potential sensory protein coding regions in a genome for that particular bacterial strain for the microbiome sample from which the sequenced metagenomic reads were obtained; calculating the sensory protein abundance using one of the following: calculating ratio of the count of sensors to a total metagenomic size wherein total metagenomic size is the size of the sequenced metagenomic reads constituting the microbiome sample or calculating the ratio of the covered base length of the particular strain to the total metagenomic size of the microbiome sample for each available bacterial strain; assessing the risk of the person to be in a prediabetes diseased state… categorization of the person either in a low risk or a high risk of prediabetes diseased state based on a predefined criteria…" The recited claim limitations are acts of evaluating, analyzing, observing, organizing and judging data that could be practically performed in the human mind and/or with pen and paper. For instance, creating a database could include recording the data on pen and paper.
Claim 3 recites: “a plurality of organisms having more than 90 percent identity and coverage over a genome of HTMs” This limitation is involved with comparing whether the organisms have more than 90 percent identity, which requires evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 6 recites: "…sequence alignment…identifying sequence similarity..." Aligning sequences and identifying are acts of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Mathematical concepts recited include:
Claim 1 recite: "applying, via one or more a random forest classifier on the generated sensory protein abundance profiles of case- control samples to generate a classification model... applying a Random Forest (RF) approach on the sensory protein abundance profiles of sequenced metagenomic reads," "…performing 10 replicates on 10-fold cross-validation on the training set to build 100 cross-validation RF models, capturing an importance a GINI index of each of the features feature included in cross-validation models…; …the sum of their GINI index values…; evaluating the performance of the 'evaluation' model on the basis of a balancing Score, followed by Matthews correlation coefficient (MCC) and Area under the curve (AUC) scores; and validating the final classification model on the test set containing rest 10% of the dataset earlier kept aside as the independent test set, wherein accuracy of training model and a confidence probability of a binary prediction to be case or control were accounted… quantifying abundance of a sensory protein from the sequenced metagenomic reads using the database of sensory protein sequences, wherein calculating the abundance of the sensory protein, comprises: performing a sequence alignment with the sequences in the created sensory protein sequence database as query against the sequenced metagenomic reads, wherein the hits satisfying a minimum e-value threshold of 0.00001 are considered as correct matches; computing cumulative matches of the sequenced metagenomic reads to form a count of sensors for each bacterial strain in the sensory protein sequence database, wherein the count of sensors indicates approximately the potential number of sensory protein coding regions in the genome for that particular bacterial strain for the microbiome sample from which the sequenced metagenomic reads were obtained; computing a cumulative length of the nucleotide bases for all these hits for each bacterial strain in the sensory protein sequence database to form a covered base length, wherein the covered base length indicates approximately a total length of potential sensory protein coding regions in a genome for that particular bacterial strain for the microbiome sample from which the sequenced metagenomic reads were obtained; calculating the sensory protein abundance using one of the following: calculating ratio of the count of sensors to a total metagenomic size wherein total metagenomic size is the size of the sequenced metagenomic reads constituting the microbiome sample, or calculating the ratio of the covered base length of the particular strain to the total metagenomic size of the microbiome sample for each available bacterial strain…" The recited claim limitations are mathematical concepts and/or formulas.
Claim 6 recites: "… wherein the sequence alignment is performed using one or more of Basic Local Alignment Search Tool (BLAST), BLAST-like alignment tool (BLAT), DIAMOND alignment tool, RAPSearch tool, Burrows-Wheeler Aligner (BWA), Bowtie or through use of clustering algorithms comprising BLASTCLUST, CLUSTAL W, VSEARCH or heuristic techniques of identifying sequence similarity." The recited claim limitations are mathematical concepts and/or formulas.
Law of nature recited include:
Claim 1 recite: "assessing the risk of the person to be in a prediabetes diseased state using the classification model and the quantified abundance of the sensory protein in the metagenomic sample of the person, wherein the assessment results in categorization of the person either in a low risk or a high risk of prediabetes diseased state based on a predefined criteria; and administering a therapeutic construct to the person depending on the risk of the prediabetes…" The claim element recites a correlation between the quantified abundance of the sensory protein in the metagenomic sample of the person and a risk of prediabetes, which is a law of nature because it describes a consequence of natural processes in the human body, e.g., the naturally-occurring relationship between the quantified abundance of the sensory protein in the metagenomic sample of the person and a risk of prediabetes.
Certain methods of organizing human activity recited include:
Claim 1 recite: "administering a therapeutic construct to the person depending on the risk of the prediabetes." The process of administering a therapeutic construct to the person is involved with providing rules or instructions to be followed.
As indicated above, claim 1 recites limitations that falls under the "Mental processes" grouping of abstract ideas. Examples of mental processes in claim 1 are assessing the risk of the person to be in the prediabetes diseased state; identifying all the annotated sensory proteins; selecting a random set of sequenced metagenomic reads; categorization of the person either in a low risk or a high risk of prediabetes diseased state; computing and calculating ratio of the count of sensors. These claim elements are involved with acts of evaluating, analyzing, observing and judging data. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2) subsection III). Although, Claim 1 recite performing the method as part of a method executed on a computer, there are no additional limitations to indicate that anything other than a generic computer is required. However, merely requiring that the steps are carried out with a generic computer does not negate the mental nature of these steps and equates rather to merely using a computer as a tool to perform the mental process. Therefore, under the broadest reasonable interpretation, the indicated claims above can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "Mental processes" grouping of abstract ideas.
Claims 1 and 6 recite mathematical concepts and formulas as discussed above. The random forest classifier, probability, quantifying, calculating and computing are mathematical concepts and/or formulas that falls under the “mathematical concepts” grouping of abstract ideas.
The claim limitations of claim 1 recites a correlation between the quantified abundance of the sensory protein in the metagenomic sample of the person and a risk of prediabetes, which is a law of nature because it describes a consequence of natural processes in the human body, e.g., the naturally-occurring relationship between the quantified abundance of the sensory protein in the metagenomic sample of the person and a risk of prediabetes.
The limitation of claim 1 is involved with a process of administering a therapeutic construct to the person, which is providing rules or instructions to be followed that falls under the abstract idea of "certain methods of organizing human activity."
As such, claims 1, 3 and 6-7 recite an abstract idea (Step 2A, Prong 1: YES).
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 or not (Step 2A, Prong 2). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional elements that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claim 1 recites "creating a database of sensory protein sequences of a plurality of organisms, wherein the database of sensory protein sequences comprises information pertaining to the sensory protein of all fully or partially sequenced bacterial genomes obtained from a plurality of public repositories…, extracting a data from the plurality of public repositories," "generating sensory protein abundance profiles of case-control samples obtained from publicly available data," “collecting a microbiome sample from fecal sample of the person for the assessing the risk of prediabetes,” “extracting DNA from the microbial cells; sequencing, via a sequencer, using the extracted DNA to get sequenced metagenomic reads” and “administering a therapeutic construct to the person depending on the risk of the prediabetes.” Also, claim 1 recites “applying, via one or more hardware processors, a random forest classifier…,” and “collating the sensory protein sequences and the sequences identified through sequence alignment to create the sensory protein sequence database...."
Claim 3 recites wherein the one or more organisms are non-pathogenic, one or more of a natural or synthetically derived compounds which boost the population of HTMs, wherein the natural or synthetically derived compounds are non-toxic, or one or more of a natural or synthetically derived compounds which targets the Disease Markers (DMs), wherein the natural or synthetically derived compounds are non-toxic and do not cause any adverse effect.
Claim 7 recites wherein the plurality of public repositories comprises one or more of NCBI database, Protein Data Bank, KEGG database, PFAM database or EggNOG.
The elements of claims 1 and 3 as indicated above equate to insignificant extra solutional activities of data gathering. Data gathering serves as input to the recited judicial exception in the claims. The collected data are required to perform the judicial exception in claim 1 of assessing the risk of a person having prediabetes. Claim 1 also recites "applying, via one or more hardware processors, a random forest classifier…," The hardware processor equates to generic computer components. Claim 1 invokes the computer components merely as tools to execute the abstract idea. The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. (see MPEP 2106.05(f)). Additionally, the listed additional elements are mere instructions to apply an exception because they recite no more than an idea of a solution or outcome and does not recite a technological solution to a technological problem. (See MPEP 2106.05(f)(1)).
The limitation in claim 1 of administering a therapeutic construct to the person depending on the risk of the prediabetes applies the exception in a generic way and does not integrate the recited exception into a practical application (see MPEP 2106.04(d)(2)). Also, this limitation is being interpreted as a contingent limitation, as denoted by the phrase “administered … depending on the risk”. MPEP 2111.04.II recites “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” For instance, when the risk of prediabetes is “a low risk”, then the therapeutic construct is not administered.
Regarding the limitation in claim 1 of “wherein the method is implemented for assessing and treating the prediabetes in the person by modulating a disease microbiome composition”, this limitation is being interpreted as intended use and is thus not required to be performed by the claim.
As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1, 3 and 6-7 are directed to an abstract idea (Step 2A, Prong 2: NO).
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 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claim 1 recites "creating a database of sensory protein sequences of a plurality of organisms, wherein the database of sensory protein sequences comprises information pertaining to the sensory protein of all fully or partially sequenced bacterial genomes obtained from a plurality of public repositories…, extracting a data from the plurality of public repositories," "generating sensory protein abundance profiles of case-control samples obtained from publicly available data," “collecting a microbiome sample from fecal sample of the person for the assessing the risk of prediabetes,” “extracting DNA from the microbial cells; sequencing, via a sequencer, using the extracted DNA to get sequenced metagenomic reads” and “administering a therapeutic construct to the person depending on the risk of the prediabetes.” Also, claim 1 recites “applying, via one or more hardware processors, a random forest classifier…,” and “collating the sensory protein sequences and the sequences identified through sequence alignment to create the sensory protein sequence database...."
Claim 3 recites wherein the one or more organisms are non-pathogenic, one or more of a natural or synthetically derived compounds which boost the population of HTMs, wherein the natural or synthetically derived compounds are non-toxic, or one or more of a natural or synthetically derived compounds which targets the Disease Markers (DMs), wherein the natural or synthetically derived compounds are non-toxic and do not cause any adverse effect.
Claim 7 recites wherein the plurality of public repositories comprises one or more of NCBI database, Protein Data Bank, KEGG database, PFAM database or EggNOG.
The additional elements indicated above do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. The limitations equate to mere data gathering activities, which are insignificant extra solutional activities. The quantified abundance of the sensory protein in the metagenomic sample of the person equates to determining the level of biomarkers. The courts have recognized that techniques for determining the level of a biomarker in blood by any means; analyzing DNA to provide sequence information or detect allelic variants; amplifying and sequencing nucleic acid sequences and detecting DNA or enzymes in a sample as well-understood, routine, conventional activities in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. (See MPEP 2106.05(d)). As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. (see MPEP 2106.05(g)). Also, limitations that equate to mere data gathering and outputting via generic computer components, such as receiving data at a computer or outputting data, amount to insignificant extra-solution activity as set forth by the courts in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and OIP Techs., Inc, v, Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Also, the additional elements include storing and retrieving information in memory. Storing and retrieving information in memory were identified by the courts as well-understood, routine and conventional in 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. Also, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more as identified by the courts in Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1, 3 and 6-7 are not patent eligible.
Response to Arguments under 35 USC 101, pages 16-26
Applicant's arguments filed 01/12/2026 have been fully considered but they are not persuasive.
Claims 1, 3 and 6 are amended. Claims 2, 4-5 and 8-11 are cancelled.
It is noted that arguments are based on amended claims.
Applicant argues that under step 2A, prong 2 of the 101 analysis, claim 1 recites a practical application of a particular treatment or prophylaxis for a disease or medical condition because a therapeutic construct is administered to a person depending on their risk of prediabetes.
In response, Applicant’s argument is not persuasive. Claim 1 recites the phrase “…categorization of the person either in a low risk or a high risk of prediabetes diseased state based on a predefined criteria; and administering a therapeutic construct to the person depending on the risk of the prediabetes”. This limitation equates to a contingent limitation because the construct is administered depending on the risk of prediabetes and may not be administered when there is “a low risk”. As indicated in MPEP 2111.04.II, “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” In the instant case, a therapeutic construct is not administered to the person when their risk of being in a prediabetes diseased state is low. Therefore, the recited administering a therapeutic construct to the person depending on the risk of the prediabetes is equivalent to mere instructions to apply an exception because they recite no more than an idea of a solution or outcome that does not recite a technological solution to a technological problem.
Furthermore, the limitation in claim 1 of “wherein the method is implemented for assessing and treating the prediabetes in the person by modulating a disease microbiome composition” is interpreted as an intended use and is thus not required by the claim. Even if this limitation were required by the claim, it would not constitute a particular treatment because “by modulating” does not recite a particular type of treatment.
Applicant asserts that the claimed subject matter is similar to Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1066–68, 100 USPQ2d 1492, 1500-01 (Fed. Cir. 2011) because the claims include a physical step of administering a therapeutic construct to the person depending on the risk of the prediabetes for the treatment of a particular medical condition such as prediabetes.
In response, Applicants arguments are not persuasive because In Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1066–68, 100 USPQ2d 1492, 1500-01 (Fed. Cir. 2011), the step of immunization integrates an abstract idea into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, which is considered to be a particular prophylaxis limitation that practically applies the abstract idea. In the instant case, a therapeutic construct is not administered to the person when their risk of being in a prediabetes diseased state is low. Therefore, the recited administering a therapeutic construct to the person depending on the risk of the prediabetes is equivalent to mere instructions to apply an exception because they recite no more than an idea of a solution or outcome that does not recite a technological solution to a technological problem.
Applicant also argues that integration of a judicial exception into a practical application is
achieved in terms of improving the functionality of the computer and computing technology with the capability of one-time creation of the database of sensory protein sequences and one-time creation of classification model and the specific implementation of steps to calculate the abundance of the sensory protein.
In response, Applicant’s not persuasive because the argument is a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. From the asserted improvement, it is not clear how the claimed invention improves over existing technology and it is also not clear how one would gauge the improvement since there are no metrics for comparison between the claimed technology and previous technology. Overall, one of ordinary skill in the art cannot gauge whether the improvements asserted are delivered by the claims because the details provided in the specification do not provide sufficient details such that the improvement would be apparent, do not explain the details of an unconventional technical solution expressed in the claim, or identify technical improvements realized by the claim over the prior art. As stated in MPEP 2106.05(a) and MPEP 2106.04(d), the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Furthermore, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. (see MPEP 2106.05(a) and MPEP 2106.04(d)).
Applicant argues that the claims are similar to Subject Matter Eligibility Example 39. Applicant argues that the claims are based on math but do not recite mathematical concepts.
In response, Applicant’s argument is not persuasive because claim 1 recites mathematical concepts and formulas, which includes quantifying, computing and calculating. Claim 1 recites (page 4, quantifying step) “quantifying sensory protein abundance from the sequenced metagenomic reads using the database of sensory protein sequences, wherein calculating the abundance of the sensory protein, comprises: performing a sequence alignment with the sequences in the created sensory protein sequence database as query against the sequenced metagenomic reads, wherein the hits satisfying a minimum e-value threshold of 0.00001 are considered as correct matches; computing cumulative matches of the sequenced metagenomic reads to form a count of sensors for each bacterial strain in the sensory protein sequence database, wherein the count of sensors indicates approximately the potential number of sensory protein coding regions in the genome for that particular bacterial strain for the microbiome sample from which the sequenced metagenomic reads were obtained; computing a cumulative length of the nucleotide bases for all these hits for each bacterial strain in the sensory protein sequence database to form a covered base length, wherein the covered base length indicates approximately a total length of potential sensory protein coding regions in a genome for that particular bacterial strain for the microbiome sample from which the sequenced metagenomic reads were obtained; calculating the sensory protein abundance using one of the following: calculating ratio of the count of sensors to a total metagenomic size wherein total metagenomic size is the size of the sequenced metagenomic reads constituting the microbiome sample…”.
Applicant argues the claims do not recite a mental process because they cannot be performed in the human mind. Rather the claims are performed by hardware processors. Applicant argues that the following limitations cannot be performed by a human: “selecting a random set of sequenced metagenomic reads comprising 90% of microbiome samples as a training set and a remaining 10% as a test set; performing 10 replicates on 10-fold cross-validation on the training set to build 100 cross-validation RF models”.
In response, Applicant’s argument is not persuasive. Claim 1 recites “selecting a random set of sequenced metagenomic reads comprising 90% of the microbiome samples as a training set and rest of the 10% were considered as a test set” that is involved with analyzing, evaluating and selecting data. The BRI of selecting a random set of sequenced metagenomic reads includes a human making a determination as to which microbiome samples will be part of the training or test set. Acts of evaluating and analyzing data that could be practically performed in the human mind and/or with pen and paper and there is no indication in the claims that the process or amount of data is too complicated or too large to be performed by the human mind and/or with pen and paper. Also, MPEP 2106.04(a)(2)(III)(C) recites “Claims can recite a mental process even if they are claimed as being performed on a computer.”
Applicant refers to XY, LLC v. Trans Ova Genetics, 968 F.3d 1323, 1330-32 (Fed. Cir. 2020), the Federal Circuit determined that claims to a method of operating a flow cytometry apparatus to classify and sort particles into at least two populations in real time to more accurately classify similar particles as not directed to "the abstract idea of using a 'mathematical equation that permits rotating multidimensional data"' even though they may have involved mathematical concepts. Applicant argues that the subject matter of efficiently creating a database of sensory protein sequences of a plurality of organisms and generating a classification model to reduce the risk of prediabetes in a person is not directed to "the abstract idea of using a mathematical equation" even though they may have involved mathematical concepts, which is similar to the XY, LLC v. Trans Ova Genetics.
In response, Applicant’s argument is not persuasive because it is unclear what is meant by efficiently creating a database. The database does not appear to improve the functioning of a computer or improve any other technology or technical field.
Applicant makes an argument under Step 2B, which appears to be an argument under Step 2A, Prong 2. Applicant argues that the claimed subject matter recites the technical advancement in terms of one-time creation of the database of sensory protein sequences and one-time creation of classification model. Applicant also argues that the claims recite a technical advancement of early intervention by assessing risk of prediabetes and treating prediabetes.
In response, Applicant’s arguments are not persuasive. The limitation in claim 1 of “assessing … the risk of the person to be in a prediabetes diseased state using the classification model and quantified abundance of the sensory protein” has been identified as a judicial exception. The improvement to a technology or technical field cannot be the result of the judicial exception itself (MPEP 2106.05(a)). It is unclear how a one-time creation of the database of sensory protein sequences and one-time creation of classification model provide the technical advancement. Applicant’s argument of improvement is a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art as discussed above.
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.
Claims 1, 3 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Bajaj (US 2014/0179726 A1, published Jun. 26, 2014; as cited on the 02/11/2022 IDS Document) in view of Backhed (US 2015/0317444 A1, published Nov. 05, 2015; as cited on the 02/11/2022 IDS Document), Lima ("HAMAP: a database of completely sequenced microbial proteome sets and manually curated microbial protein families in UniProtKB/Swiss-Prot." Nucleic acids research 37.suppl_1 (2009): D471-D478., as cited on the attached 892 form); Loomba ("Gut microbiome-based metagenomic signature for non-invasive detection of advanced fibrosis in human nonalcoholic fatty liver disease." Cell metabolism 25.5 (2017): 1054-1062.; as cited on the 10/02/2025 892 form); Chen ("Gene expression profiling gut microbiota in different races of humans."; as cited on the 10/02/2025 892 form) and Stritzker (US 2008/0193373 A1, published Aug. 14, 2008; as cited on the 02/11/2022 IDS Document).
Regarding independent claim 1, Bajaj teaches creating a database of sensory protein sequences of a plurality of organisms wherein the database of sensory protein sequences comprises information pertaining to a sensory protein of all fully or partially sequenced bacterial genomes obtained from a plurality of public repositories, wherein creation of the database of sensory protein sequences is a one-time process and created before a test microbiome sample from the person is provided for diagnosis and therapeutic purposes with “Further embodiments of the invention provide methods for determining reference microfloral signatures, and databases comprising the same. Such signatures constitute prototypes or models for use as references when the assessment of an individual's microflora is undertaken. In some embodiments, control signatures are collected and averaged or amalgamated to develop reference signatures which are correlated with a disease or condition of interest (and/or with the absence of the disease/condition). The reference signatures may be in the form of e.g. sequences which are characteristic of particular bacterial types, according to any useful classification (phylum, order, class, genus, species, strain, type, etc.) Further, the reference signatures generally include this information for relevant groups and subgroups of microflora, e.g. those associated with a particular disease, condition, etc. The characteristics of the reference signatures are generally recorded (stored, compiled, etc.) in an electronic computerized catalog, library, database, etc. that is accessible to a practitioner of the invention. Such databases may include the Ribosomal Database versions 8 and 10, Greengenes, and Genbank. The invention also encompasses computer programs (e.g. executable software programs, and/or computers configured to carry out the programs), which enable a practitioner to enter analytical data into the system (e.g. the results of rRNA PCR amplification of a stool sample, which may be the patient's “signature”) and to carry out a comparison to the stored reference signatures. Output from the program may include an expression of the level of similarity between the patient's signature and one or more relevant stored reference signatures, and/or the statistical likelihood that the patient already has or is likely to develop a disease or condition associated with one or more reference signatures.” (para. [0047]) and with “In yet other embodiments of the invention, the gut signature of a subject may be, or may include, results obtained by analyzing the protein content of a biological sample (e.g. a gut sample), of a subject. The results may include the identity of the proteins, the presence or absence of selected proteins, the relative abundance of the proteins (e.g. compared to suitable controls), etc. The proteins may be associated with (e.g. characteristic of) one or more bacterial (or other microfloral) taxa of interest. Exemplary proteins that may be included in such a gut proteome signature include but are not limited to those which are known to those of skill in the art.” (para. [0049]). It is prima facie obvious that a database is first created before data can be stored.
Bajaj teaches extracting a data from the plurality of public repositories; identifying all the annotated sensory proteins from the extracted data using a set of keyword searches; performing a sequence alignment to identify a set of annotated or characterized sensory protein sequences; filtering the results of the sequence alignment based on 95% identity, 95% coverage and an e-value cut-off 0.00001 to identify a set of additional sensory protein sequences; and collating the sensory protein sequences and the sequences identified through sequence alignment to create the sensory protein sequence database with “Interrogation of the Microbiome: Stool was collected and DNA extracted for microbiome analysis within 24 hours of collection from patients and controls using published techniques (29). We first routinely use Length Heterogeneity PCR (LH-PCR) fingerprinting of the 16S rRNA to rapidly survey our samples and standardize the community amplification. We then interrogated the microbial taxa associated with the gut fecal microbiome using Multitag Pyrosequencing (MTPS). This technique allows the rapid sequencing of multiple samples at one time yielding thousands of sequence reads per sample (12).” (para. [0060]); “Microbiome Community Fingerprinting: LH-PCR was done to standardize the community analysis as previously published (21). Briefly, total genomic DNA was extracted from tissue using Bio101 kit from MP Biomedicals Inc., Montreal, Quebec as per the manufacturer's instructions. About 10 ng of extracted DNA was amplified by PCR using a fluorescently labeled forward primer 27F (5’-(6FAM) AGAGTTTGATCCTGGCTCA G-3′, SEQ ID NO: 1) and unlabeled reverse primer 355R′ (5′-GCTGCCTCCCGTAGGAGT-3′, SEQ ID NO: 2). Both primers are universal primers for Bacteria (22). The LH-PCR products were diluted according to their intensity on agarose gel electrophoresis and mixed with ILS-600 size standards (Promega) and HiDi Formamide (Applied Biosystems, Foster City, Calif.). The diluted samples were then separated on a ABI 3130x1 fluorescent capillary sequencer (Applied Biosystems, Foster City, Calif.) and processed using the Genemapper™ software package (Applied Biosystems, Foster City, Calif.). Normalized peak areas were calculated using a custom PERL script and OTUs constituting less than 1% of the total community from each sample were eliminated from the analysis to remove the variable low abundance components within the communities.” (para. [0061]) and “MTPS: We employed the MTPS process to characterize the microbiome from the fecal samples. Specifically, we have generated a set of 96 emulsion PCR fusion primers that contain the 454 emulsion PCR linkers on the 27F and 355R primers and a different 8 base “barcode” between the A adapter and 27F primer. Thus, each fecal sample was amplified with unique bar-coded forward 16S rRNA primers and then up to 96 samples were pooled and subjected to emulsion PCR and pyrosequenced using a GS-FLX pyrosequencer (Roche). Data from each pooled sample were “deconvoluted” by sorting the sequences into bins based on the barcodes using custom PERL scripts. Thus, we were able to normalize each sample by the total number of reads from each barcode. We have noted that ligating tagged primers to PCR amplicons distorts the abundances of the communities and thus it is critical to incorporate the tags during the original amplification step (12). Several groups have employed various barcoding strategies to analyze multiple samples and this strategy is now well accepted (38).” (para. [0062]) and “Microbiome Community Fingerprinting: LH-PCR was done to standardize the community analysis as previously published. Briefly, total genomic DNA was extracted from tissue using Bio101 kit from MP Biomedicals Inc., Montreal, Quebec as per the manufacturer's instructions. About 10 ng of extracted DNA was amplified by PCR using a fluorescently labeled forward primer 27F (5′-(6FAM) AGAGTTTGATCCTGGCTCA G-3′, SEQ ID NO: 1) and unlabeled reverse primer 355R′ (5′-GCTGCCTCCCGTAGGAGT-3′, SEQ ID NO: 2). Both primers are universal primers for Bacteria (23). The LH-PCR products were diluted according to their intensity on agarose gel electrophoresis and mixed with ILS-600 size standards (Promega) and HiDi Formamide (Applied Biosystems, Foster City, Calif.). The diluted samples were then separated on a ABI 3130x1 fluorescent capillary sequencer (Applied Biosystems, Foster City, Calif.) and processed using the Genemapper™ software package (Applied Biosystems, Foster City, Calif.). Normalized peak areas were calculated using a custom PERL script and operational taxonomic units (OTUs) constituting less than 1% of the total community from each sample were eliminated from the analysis to remove the variable low abundance components within the communities.” (para. [0140]).
Bajaj teaches generating sensory protein abundance profiles of case-control samples obtained from publicly available data with “Further embodiments of the invention provide methods for determining reference microfloral signatures, and databases comprising the same. Such signatures constitute prototypes or models for use as references when the assessment of an individual's microflora is undertaken. In some embodiments, control signatures are collected and averaged or amalgamated to develop reference signatures which are correlated with a disease or condition of interest (and/or with the absence of the disease/condition). The reference signatures may be in the form of e.g. sequences which are characteristic of particular bacterial types, according to any useful classification (phylum, order, class, genus, species, strain, type, etc.) Further, the reference signatures generally include this information for relevant groups and subgroups of microflora, e.g. those associated with a particular disease, condition, etc. The characteristics of the reference signatures are generally recorded (stored, compiled, etc.) in an electronic computerized catalog, library, database, etc. that is accessible to a practitioner of the invention. Such databases may include the Ribosomal Database versions 8 and 10, Greengenes, and Genbank. The invention also encompasses computer programs (e.g. executable software programs, and/or computers configured to carry out the programs), which enable a practitioner to enter analytical data into the system (e.g. the results of rRNA PCR amplification of a stool sample, which may be the patient's “signature”) and to carry out a comparison to the stored reference signatures. Output from the program may include an expression of the level of similarity between the patient's signature and one or more relevant stored reference signatures, and/or the statistical likelihood that the patient already has or is likely to develop a disease or condition associated with one or more reference signatures” (para. [0047])
Bajaj teaches collecting a microbiome sample from fecal sample of the person with “MTPS: We employed the MTPS process to characterize the microbiome from the fecal samples. Specifically, we have generated a set of 96 emulsion PCR fusion primers that contain the 454 emulsion PCR linkers on the 27F and 355R primers and a different 8 base “barcode” between the A adapter and 27F primer. Thus, each fecal sample was amplified with unique bar-coded forward 16S rRNA primers and then up to 96 samples were pooled and subjected to emulsion PCR and pyrosequenced using a GS-FLX pyrosequencer (Roche). Data from each pooled sample were “deconvoluted” by sorting the sequences into bins based on the barcodes using custom PERL scripts. Thus, we were able to normalize each sample by the total number of reads from each barcode. We have noted that ligating tagged primers to PCR amplicons distorts the abundances of the communities and thus it is critical to incorporate the tags during the original amplification step (12). Several groups have employed various barcoding strategies to analyze multiple samples and this strategy is now well accepted (38).” (Para. [0062]).
Backhed also teaches collecting a microbiome sample from fecal sample of the person for assessing the risk of prediabetes, wherein the microbiome sample comprising microbial cells with “Thus, a method is disclosed for identifying an individual having or at risk of T2D, comprising obtaining a gastro intestinal sample from said individual, for example a fecal sample representing the gastro intestinal ecosystem, and determining the amount of specific microbial genera, species or metagenomic clusters in the sample of said individual.” (para. [0019]).
Bajaj teaches extracting DNA from the microbial cells; sequencing, via a sequencer, using the extracted DNA to get sequenced metagenomic reads; with “Interrogation of the Microbiome: Stool was collected and DNA extracted for microbiome analysis within 24 hours of collection from patients and controls using published techniques (29). We first routinely use Length Heterogeneity PCR (LH-PCR) fingerprinting of the 16S rRNA to rapidly survey our samples and standardize the community amplification. We then interrogated the microbial taxa associated with the gut fecal microbiome using Multitag Pyrosequencing (MTPS). This technique allows the rapid sequencing of multiple samples at one time yielding thousands of sequence reads per sample (12).” (Para. [0060]) and with “Interrogation of the Microbiome: Stool was collected and DNA extracted for microbiome analysis using published techniques (30). A subset underwent an un-sedated, unprepared flexible sigmoidoscopy during which a pinch biopsy of the recto-sigmoid mucosa was obtained, which was snap-frozen and stored at −80° C. till the analysis. We first use Length Heterogeneity PCR (LH-PCR) fingerprinting of the 16S rRNA to rapidly survey samples and standardize the community amplification. We then interrogated the microbial taxa associated with the gut fecal microbiome using Multitag Pyrosequencing (MTPS) (16). This technique allows for rapid sequencing of multiple samples at one time yielding thousands of sequence reads per sample.” (Para. [0139]).
Backhed also teaches extracting DNA from the microbial cells; sequencing, via a sequencer, using the extracted DNA to get sequenced metagenomic reads; with “To characterize the composition of the gut microbiota associated with T2D, the fecal microbiota of the selected population is analyzed. The cohort is selected with a stratified randomized method from a population-based screening sample (12, 13), resulting in subgroups: persons who have T2D, IGT or are healthy (normal glucose tolerance, NGT). Genomic DNA is extracted with a standard procedure (14) and sequenced, preferably on Illumina HiSeq 2000.” (Para. [0054]) and with Claim 37. Claim 37 of Backhed recites “The computer program product of claim 32, wherein construction of the computer program product is further configured to: (i) select the population group to be studied; (ii) obtain gut metagenomic sequence data from said population; and (iii) identify MGCs from all the metagenomic sequence data from said population.” (claim 37 of Backhed).
Bajaj teaches quantifying abundance of a sensory protein from the sequenced metagenomic reads using the database of sensory protein sequences with “In one embodiment, the present invention provides methods for diagnosing patients at risk for developing a disease or condition correlated with the presence or absence of (and/or the relative distribution of) particular taxa of microbes in the gut, or in a particular component of or location within the gut. Such patients may have a higher than average or higher than normal chance of developing overt symptoms of the disease or condition, compared to individuals who have different gut microbes, or different amounts of microbes, or different relative amounts of microbes. Early identification of such a propensity allows early intervention, e.g. by altering the identity and/or the relative abundance of gut microflora associated with, and possibly causing, the disease/condition, so that development of the disease/condition may be avoided, or delayed, or the associated symptoms may be lessened.” (para. [0030]) and “In yet other embodiments of the invention, the gut signature of a subject may be, or may include, results obtained by analyzing the protein content of a biological sample (e.g. a gut sample), of a subject. The results may include the identity of the proteins, the presence or absence of selected proteins, the relative abundance of the proteins (e.g. compared to suitable controls), etc. The proteins may be associated with (e.g. characteristic of) one or more bacterial (or other microfloral) taxa of interest. Exemplary proteins that may be included in such a gut proteome signature include but are not limited to those which are known to those of skill in the art.” (para. [0049]).
Bajaj teaches performing a sequence alignment with the sequences in the created sensory protein sequence database as query against the sequenced metagenomic reads, wherein the hits satisfying a minimum e-value threshold of 0.00001 are considered as correct matches with “The present invention provides methods of assessing the presence or the risk of development of encephalopathy in a patient with liver disease. The methods comprise the steps of 1) analyzing gut microflora of said patient in order to determine a gut microbiome signature for said patient; 2) comparing said gut microbiome signature of said patient to one or more gut microbiome reference signatures, wherein said one or more gut microbiome reference signatures include at least one of a positive gut microbiome reference signature based on results from control subjects with encephalopathy and a negative gut microbiome reference signature based on results from control subjects without encephalopathy; and if said gut microbiome signature for said patient statistically significantly matches said positive gut microbiome reference signature, (e.g. includes the same types and/or the same relative abundances, ratios, etc. of microflora in statistically significant amounts), then concluding that said patient has or is at risk of developing encephalopathy; and/or if said gut microbiome signature for said patient statistically significantly matches said negative gut microbiome reference signature, then concluding that said patient does not have or is not at risk of developing encephalopathy. In some embodiments, a statistically significant match has a P value of 0.05 or less. In some embodiments, the gut microflora is analyzed in a biological sample preferably selected from a stool sample, a sample of the lumen content, a mucosal biopsy sample, an oral sample, a blood sample and a urine sample. In other embodiments, the gut microbiome signature may include one or more of: bacterial taxa identified in said biological sample; bacterial metabolic products in said biological sample; and proteins in said biological sample. In yet other embodiments, the gut microbiome signature is based on an analysis of amplification products of DNA and/or RNA of said gut microflora, e.g. is based on an analysis of amplification products of genes coding for one or more of: Small Subunit rRNA, Intervening Transcribed Spacer, and Large Subunit rRNA. In some embodiments, the gut microbiome signature includes results obtained by assaying the mRNA composition of said biological samples. In some embodiments, the liver disease is cirrhosis and the encephalopathy is hepatic encephalopathy (HE)…” (para. [0010]).
Bajaj teaches computing the cumulative matches of the sequenced metagenomic reads to form a count of sensors for each bacterial strain in the sensory protein sequence database, wherein the count of sensors indicates approximately the potential number of sensory protein coding regions in the genome for that particular bacterial strain for the microbiome sample from which the sequenced metagenomic reads were obtained with “After a sample is obtained, the types and/or the quantity (e.g. occurrence) in the sample of at least one microbe of interest is determined. In addition, a total amount of microbes may be determined, and then for each constituent microbe, a fractional percentage (e.g. relative amount, ratio, distribution, frequency, percentage, etc.) of the total is calculated. The result is typically correlated with at least one suitable control result, e.g. control results of the same parameter(s) obtained from healthy individuals (negative control), and/or individuals known to have a disease or condition of interest (positive control), or from subjects who have had the disease and condition of interest and are being or have been treated, either successfully or unsuccessfully, etc.” (para. [0034]) and with “In yet other embodiments of the invention, the gut signature of a subject may be, or may include, results obtained by analyzing the protein content of a biological sample (e.g. a gut sample), of a subject. The results may include the identity of the proteins, the presence or absence of selected proteins, the relative abundance of the proteins (e.g. compared to suitable controls), etc. The proteins may be associated with (e.g. characteristic of) one or more bacterial (or other microfloral) taxa of interest. Exemplary proteins that may be included in such a gut proteome signature include but are not limited to those which are known to those of skill in the art.” (para. [0049])
Bajaj teaches computing the cumulative length of the nucleotide bases for all these hits for each bacterial strain in the sensory protein sequence database to form a covered base length, wherein the covered base length indicates approximately the total length of the potential sensory protein coding regions in the genome for that particular bacterial strain for the microbiome sample from which the sequenced metagenomic reads were obtained; calculating the sensory protein abundance using one of the following: calculating ratio of the count of sensors to the total metagenomic size (in Megabases) wherein total metagenomic size (in Megabases) is the size of the sequenced metagenomic reads constituting the microbiome sample, or calculating the ratio of the covered base length of the particular strain to the total metagenomic size (in Megabases) of the microbiome sample for each available bacterial strain with “…The methods comprise the steps of 1) analyzing gut microflora of said patient in order to determine a gut microbiome signature for said patient; 2) comparing said gut microbiome signature of said patient to one or more gut microbiome reference signatures, wherein said one or more gut microbiome reference signatures include at least one of a positive gut microbiome reference signature based on results from control subjects with encephalopathy and a negative gut microbiome reference signature based on results from control subjects without encephalopathy; and if said gut microbiome signature for said patient statistically significantly matches said positive gut microbiome reference signature, (e.g. includes the same types and/or the same relative abundances, ratios, etc. of microflora in statistically significant amounts), then concluding that said patient has or is at risk of developing encephalopathy; and/or if said gut microbiome signature for said patient statistically significantly matches said negative gut microbiome reference signature, then concluding that said patient does not have or is not at risk of developing encephalopathy. In some embodiments, a statistically significant match has a P value of 0.05 or less. In some embodiments, the gut microflora is analyzed in a biological sample preferably selected from a stool sample, a sample of the lumen content, a mucosal biopsy sample, an oral sample, a blood sample and a urine sample. In other embodiments, the gut microbiome signature may include one or more of: bacterial taxa identified in said biological sample; bacterial metabolic products in said biological sample; and proteins in said biological sample. In yet other embodiments, the gut microbiome signature is based on an analysis of amplification products of DNA and/or RNA of said gut microflora, e.g. is based on an analysis of amplification products of genes coding for one or more of: Small Subunit rRNA, Intervening Transcribed Spacer, and Large Subunit rRNA. In some embodiments, the gut microbiome signature includes results obtained by assaying the mRNA composition of said biological samples. In some embodiments of the invention, the gut microbiome signature of said patient includes an indication of the presence and/or relevant abundance of at least one of AI caligeneceae, Blautia, Burkholderia, Enterobacteriaceae, Fecalibacterium, Fusobacteriaceae, Incertae Sedis XIV, Lachnospiraceae, Porphyromonadaceae, Roseburia, Rwninococcaceae and Veillonellaceae. In other embodiments, when the gut microflora signature of said patient indicates the presence of Alcaligeneceae and Porphyromanadaceae in said gut microflora, then said concluding step results in a conclusion that said patient has or is at risk of developing encephalopathy. In other embodiments, the method further comprises the step of assessing, based on said gut microbiome signature, the presence or the risk of development of inflammation, endotoxemia, and/or endothelial dysfunction in said patient.” (para. [0010]) and with “After a sample is obtained, the types and/or the quantity (e.g. occurrence) in the sample of at least one microbe of interest is determined. In addition, a total amount of microbes may be determined, and then for each constituent microbe, a fractional percentage (e.g. relative amount, ratio, distribution, frequency, percentage, etc.) of the total is calculated. The result is typically correlated with at least one suitable control result, e.g. control results of the same parameter(s) obtained from healthy individuals (negative control), and/or individuals known to have a disease or condition of interest (positive control), or from subjects who have had the disease and condition of interest and are being or have been treated, either successfully or unsuccessfully, etc.” (para. [0034]).
Bajaj teaches calculating ratio of the count of sensors to the total metagenomic size (in Megabases) wherein total metagenomic size (in Megabases) is the size of the sequenced metagenomic reads constituting the microbiome sample, or calculating the ratio of the covered base length of the particular strain to the total metagenomic size (in Megabases) of the microbiome sample for each available bacterial strain with “…The methods comprise the steps of 1) analyzing gut microflora of said patient in order to determine a gut microbiome signature for said patient; 2) comparing said gut microbiome signature of said patient to one or more gut microbiome reference signatures, wherein said one or more gut microbiome reference signatures include at least one of a positive gut microbiome reference signature based on results from control subjects with encephalopathy and a negative gut microbiome reference signature based on results from control subjects without encephalopathy; and if said gut microbiome signature for said patient statistically significantly matches said positive gut microbiome reference signature, (e.g. includes the same types and/or the same relative abundances, ratios, etc. of microflora in statistically significant amounts), then concluding that said patient has or is at risk of developing encephalopathy; and/or if said gut microbiome signature for said patient statistically significantly matches said negative gut microbiome reference signature, then concluding that said patient does not have or is not at risk of developing encephalopathy. In some embodiments, a statistically significant match has a P value of 0.05 or less. In some embodiments, the gut microflora is analyzed in a biological sample preferably selected from a stool sample, a sample of the lumen content, a mucosal biopsy sample, an oral sample, a blood sample and a urine sample. In other embodiments, the gut microbiome signature may include one or more of: bacterial taxa identified in said biological sample; bacterial metabolic products in said biological sample; and proteins in said biological sample. In yet other embodiments, the gut microbiome signature is based on an analysis of amplification products of DNA and/or RNA of said gut microflora, e.g. is based on an analysis of amplification products of genes coding for one or more of: Small Subunit rRNA, Intervening Transcribed Spacer, and Large Subunit rRNA. In some embodiments, the gut microbiome signature includes results obtained by assaying the mRNA composition of said biological samples. In some embodiments of the invention, the gut microbiome signature of said patient includes an indication of the presence and/or relevant abundance of at least one of AI caligeneceae, Blautia, Burkholderia, Enterobacteriaceae, Fecalibacterium, Fusobacteriaceae, Incertae Sedis XIV, Lachnospiraceae, Porphyromonadaceae, Roseburia, Rwninococcaceae and Veillonellaceae. In other embodiments, when the gut microflora signature of said patient indicates the presence of Alcaligeneceae and Porphyromanadaceae in said gut microflora, then said concluding step results in a conclusion that said patient has or is at risk of developing encephalopathy. In other embodiments, the method further comprises the step of assessing, based on said gut microbiome signature, the presence or the risk of development of inflammation, endotoxemia, and/or endothelial dysfunction in said patient.” (para. [0010]) and with “After a sample is obtained, the types and/or the quantity (e.g. occurrence) in the sample of at least one microbe of interest is determined. In addition, a total amount of microbes may be determined, and then for each constituent microbe, a fractional percentage (e.g. relative amount, ratio, distribution, frequency, percentage, etc.) of the total is calculated. The result is typically correlated with at least one suitable control result, e.g. control results of the same parameter(s) obtained from healthy individuals (negative control), and/or individuals known to have a disease or condition of interest (positive control), or from subjects who have had the disease and condition of interest and are being or have been treated, either successfully or unsuccessfully, etc.” (para. [0034]). The recited “calculating the ratio of the covered base length of the particular strain to the total metagenomic size (in Megabases) of the microbiome sample for each available bacterial strain” is interpreted to correspond to “a total amount of microbes may be determined, and then for each constituent microbe, a fractional percentage (e.g. relative amount, ratio, distribution, frequency, percentage, etc.) of the total is calculated” as taught by Bajaj.
Bajaj teaches administering a therapeutic construct to the person depending on the risk of the prediabetes with “A systems biology approach is used to characterize and relate the intestinal (gut) microbiome of a host organism (e.g. a human) to physiological processes within the host. Information regarding the types and relative amounts of gut microflora is correlated with physiological processes indicative of e.g., a patient's risk of developing a disease or condition, likelihood of responding to a particular treatment, for adjusting treatment protocols, etc. The information is also used to identify novel suitable therapeutic targets and/or to develop and monitor the outcome of therapeutic treatments.” (Abstract) and with “Once a patient is identified as having, or as at high risk for developing, a disease or condition, suitable clinical intervention can be undertaken to alter the identity and/or the relative abundance of gut microflora in the individual. Accordingly, the present invention also encompasses the identification of suitable therapeutic targets for intervention and the selection/development of suitable treatment protocols. Exemplary treatments include but are not limited to: eliminating or lessening microflora associated with the condition e.g. using antibiotics or other therapies, for example, therapies that are specific for eliminating or lessening the number of targeted microflora, without affecting or minimally affecting desirable microflora, if possible; or increasing microflora that compete with the unwanted microflora, and/or which are correlated with a lack of disease symptoms, e.g. by administering probiotic and/or prebiotic supplements; by microflora) transplants (e.g. from healthy donors); by dietary modifications; by lifestyle modifications (such as increasing exercise, eliminating unhealthy behaviors such as excessive alcohol consumption, eliminating smoking, regulating sleep habits, decreasing or coping with stress, eliminating recreational drug use, etc.); by changes of diet to eliminate or lessen intake of highly processed foods; by administering probiotic substances (e.g. yogurts, kefir, fermented milk, etc.); by increasing intake of prebiotic nutrients (e.g. fructooligosaccharides such as oligofructose and inulin; galactooligosaccharides (GOS), lactulose, mannan oligosaccharides (MOS), etc., either from natural sources or in prepared forms); etc.” (para. [0044]).
Bajaj teaches wherein the therapeutic construct comprises one or more non-pathogenic Healthy Therapeutic Markers (HTMs) abundant in healthy population, a plurality of antibiotic drugs targeted against Disease Markers (DMs), prebiotics, probiotics, symbiotics, postbiotics and fecal microbiome transplant with “Once a patient is identified as having, or as at high risk for developing, a disease or condition, suitable clinical intervention can be undertaken to alter the identity and/or the relative abundance of gut microflora in the individual. Accordingly, the present invention also encompasses the identification of suitable therapeutic targets for intervention and the selection/development of suitable treatment protocols. Exemplary treatments include but are not limited to: eliminating or lessening microflora associated with the condition e.g. using antibiotics or other therapies, for example, therapies that are specific for eliminating or lessening the number of targeted microflora, without affecting or minimally affecting desirable microflora, if possible; or increasing microflora that compete with the unwanted microflora, and/or which are correlated with a lack of disease symptoms, e.g. by administering probiotic and/or prebiotic supplements; by microflora) transplants (e.g. from healthy donors); by dietary modifications; by lifestyle modifications (such as increasing exercise, eliminating unhealthy behaviors such as excessive alcohol consumption, eliminating smoking, regulating sleep habits, decreasing or coping with stress, eliminating recreational drug use, etc.); by changes of diet to eliminate or lessen intake of highly processed foods; by administering probiotic substances (e.g. yogurts, kefir, fermented milk, etc.); by increasing intake of prebiotic nutrients (e.g. fructooligosaccharides such as oligofructose and inulin; galactooligosaccharides (GOS), lactulose, mannan oligosaccharides (MOS), etc., either from natural sources or in prepared forms); etc.” (para. [0044]).
Bajaj does not explicitly teach wherein sequences corresponding to the annotated sensory proteins are used as the database and rest of obtained bacterial protein sequences are used as a query. However, this limitation is taught by Lima.
Bajaj does not explicitly teach the assessment of the risk of prediabetes; applying, via the one or more hardware processors, a random forest classifier on the generated sensory protein abundance profiles of case- control samples to generate a classification model; the risk of the person to be in the prediabetes diseased state using the classification model and the quantified abundance of the sensory protein in the metagenomic sample of the person; categorization of the person either in a low risk or a high risk of prediabetes diseased state based on a predefined criteria; applying a Random Forest (RF) approach on the sensory protein abundance profiles of sequenced metagenomic; selecting a random set of sequenced metagenomic reads comprising 90% of the fecal samples as a training set and rest of the 10% were considered as a test set; performing 10 replicates on 10-fold cross-validation on the training set to build 100 cross-validation RF models; ranking each of the features in the feature sub-set, on the basis of the sum of their GINI index values; obtaining multiple evaluation models by cumulatively adding the next ranked feature in a sub-set of features with the features of the previous evaluation model, wherein the first 'evaluation' model comprised of the top two features in the feature sub-set; assessing the performance of all the 'evaluation' models on the basis of their added features; choosing the best performing 'evaluation' model as the final classification model; evaluating the performance of the 'evaluation' model on the basis of a balancing Score and Area under the curve (AUC) scores; validating the final classification model on the test set containing rest 10% of the dataset earlier kept aside as the independent test set, wherein the accuracy of training model and the confidence probability of the binary prediction to be 'case' or 'control' were accounted in claim 1. However, these limitations are taught by Backhed.
Bajaj and Backhed does not explicitly teach GINI index and Matthews correlation coefficient (MCC) in claim. However, Loomba teaches GINI index and Chen teaches MCC.
Bajaj does not teach wherein the plurality of Healthy Therapeutic Markers (HTMs) comprises one or more of Oceanithermus profundus, Pseudoxanthomonas spadix, Rhodothermus marinus, Thermaerobacter marianensis and wherein the Disease Markers (DMs) comprise of Acholeplasma palmae in claim 1. However, this limitation is taught by Stritzker.
Backhed teaches applying, via one or more hardware processors, a random forest classifier on the generated sensory protein abundance profiles of case-control samples to generate a and generated before the test microbiome sample from the person is provided for the diagnosis and therapeutic purposes with “To use the microbiota composition to identify diabetes status a Random Forest (RF) model (Breiman, Leo (2001). “Random Forests”. Machine Learning 45 (1): 5-32. doi:10) or similar model needs to be trained in a test set of the NGT and T2D subjects. Its performance is evaluated on unseen samples from the same groups and the predictive power is scored in a receiver operator characteristic (ROC) analysis.” (para. [0056]); “Once MGCs (or species) have been identified in accordance with the invention then known normal and T2D samples from the relevant population group can be analysed or tested in order to determine which MGCs (or species) are differentially abundant between the two groups (i.e. between normal and T2D samples). An appropriate and preferred way to do this is to use a random forest (or similar) model, for example as described above.” (para. [0062]) and “Thus, a yet further embodiment of the present invention provides a model as described herein wherein (a) a random forest or similar model is used to train on a test set of normal and T2D samples to generate a predictive model for T2D; (b) using or generating a list of importance scores of the MGCs in the model; and (c) using the top scoring MGCs in the model for predicting T2D.” (para. [0063])
Backhed teaches applying a Random Forest (RF) approach on the sensory protein abundance profiles of sequenced metagenomic reads with “To use the microbiota composition to identify diabetes status a Random Forest (RF) model (Breiman, Leo (2001). “Random Forests”. Machine Learning 45 (1): 5-32. doi:10) or similar model needs to be trained in a test set of the NGT and T2D subjects. Its performance is evaluated on unseen samples from the same groups and the predictive power is scored in a receiver operator characteristic (ROC) analysis.” (para. [0056]); ]); “Once MGCs (or species) have been identified in accordance with the invention then known normal and T2D samples from the relevant population group can be analysed or tested in order to determine which MGCs (or species) are differentially abundant between the two groups (i.e. between normal and T2D samples). An appropriate and preferred way to do this is to use a random forest (or similar) model, for example as described above.” (para. [0062]) and “Thus, a yet further embodiment of the present invention provides a model as described herein wherein (a) a random forest or similar model is used to train on a test set of normal and T2D samples to generate a predictive model for T2D; (b) using or generating a list of importance scores of the MGCs in the model; and (c) using the top scoring MGCs in the model for predicting T2D.” (para. [0063]).
Backhed teaches selecting a random set of sequenced metagenomic reads comprising 90% of the fecal samples as a training set and rest of the 10% were considered as a test set; performing 10 replicates on a 10-fold cross-validation on the training set to build 100 cross-validation RF models; ranking each of the features in the feature sub-set, on the basis of the sum of their GINI index values; obtaining multiple evaluation models by cumulatively adding the next ranked feature in a sub-set of features with the features of the previous evaluation model, wherein the first evaluation model comprised of the top two features in the feature sub-set with “Thus, a yet further embodiment of the present invention provides a model as described herein wherein (a) a random forest or similar model is used to train on a test set of normal and T2D samples to generate a predictive model for T2D; (b) using or generating a list of importance scores of the MGCs in the model; and (c) using the top scoring MGCs in the model for predicting T2D.” (para. [0063]) and with “To test if the microbiota composition can identify T2D status we trained a Random Forest (RF) model in a test set of the NGT and T2D subjects. We evaluated its performance on unseen samples from the same groups and scored the predictive power in a receiver operator characteristic (ROC) analysis. The RF model generates a variable importance score for each species and MGC representing the predictive power. The importance score was used to rank species and MGCs, and the top most important ones were used in a model for predicting T2D. The discriminatory power of species and MGCs was calculated as the area under the ROC curve (AUC) (FIG. 3 a). T2D was predicted more accurately with MGCs (highest AUC=0.83, 50 MGCs) than with microbial species (highest AUC=0.71, 238 species) (FIG. 3 a, Table 3). When body mass index (BMI), waist-to-hip ratio (WHR) and waist circumference (WC) were used for predicting T2D we obtained a maximum AUC of 0.70 for WC (AUC for BMI=0.58; AUC for WHR=0.60), thus showing that the composition of the microbiota determined by MGCs correlates better with T2D than these known risk factors (22). Importantly, the T2D score obtained based on MGC clusters is similar to other published scores that combine several known risk factors for diabetes development (e.g. the FINDRISC score, validated in several countries (7)).” (para. [0122]) and with “We used our RF model trained for the discrimination of NGT and T2D individuals to stratify the 49 IGT women of the cohort. Individuals were assigned to the NGT or T2D by applying the predictive model for NGT or T2D: 10 IGT women were included in the NGT subgroup while 34 were included in the T2D subgroup (5 could not be predicted, as the probability of being either NGT or T2D was 0.5±0.02), FIG. 3 d. The characteristics of the two subgroups stratified according to faecal metagenomic profile showed that plasma levels of triglycerides and C-peptide were significantly higher in the subgroup identified as T2D than in the subgroup identified as NGT (P=0.019 and P=0.030, respectively, Wilcoxon rank sum test) (FIG. 3 e,f).” (para. [0124]) and with “FIG. 3: Classification of diabetes status by abundance of species and MGCs. a, Classification performance of a random forest model using species or MGC abundance assessed by area under the receiver-operating characteristic curve (AUC). The performance was explored for different numbers of explanatory variables, ordered in importance. The lower line shows the results obtained with species and the upper line shows the results obtained with MGCs. b, 30 most important MGCs in the predictive model using all 800 MGCs and discriminating NGT and T2D subjects. Bar length indicates the importance of the variable and colors represent enrichment in T2D (red shades, marked ‘r’) or in NGT (blue shades, marked ‘b’). c, 30 most important species in the predictive model using 915 species and discriminating NGT and T2D subjects. Bar length indicates the importance of the variable and colors represent enrichment in T2D (red shades, marked ‘r’) or in NGT (blue shades, marked ‘b’). d, Use of the model trained for discriminating NGT and T2D with MGC to predict the probability of IGT subjects being either NGT (light circles in bottom part of figure below the line) or T2D (darker circles in top part of figure above the line). e, IGT subjects predicted to be T2D (right hand column) had higher triglyceride concentration (Mann-Whitney U-test p=0.019). f, IGT subjects predicted to be T2D (right hand column) had higher C-peptide levels (Mann-Whitney U-test p=0.03).” (para. [0024]).
Backhed teaches assessing a performance of all the evaluation models on the basis of their added features; choosing the evaluation model based on the assessed performance as the final classification model with “Thus, the present invention further provides a model to identify an individual having or at risk of developing type 2 diabetes (T2D) using metagenomic clusters (MGCs) as described herein wherein said model is characterised by using different MGCs for different population groups as described herein, wherein construction of the model comprises: (i) selecting the population group to be studied; (ii) obtaining gut metagenomic sequence data from said population; and (iii) identifying MGCs from all the metagenomic sequence data from said population.” (Para. [0059]).
Backhed teaches evaluating the performance of the evaluation model on the basis of a balancing Score and Area under the curve (AUC) scores with “The model has been shown to be able to identify the risk groups with 80% accuracy or, put another way, with an area under the ROC curve (ROC AUC) of up to or greater than 0.83. There are also methods for how the model can be applied for a certain population.” (para. [0018]).
Backhed teaches validating the final classification model on the test set containing rest 10% of the dataset earlier kept aside as the independent test set, wherein accuracy of training model and a confidence probability of a binary prediction to be case or control were accounted with “To test if the microbiota composition can identify T2D status we trained a Random Forest (RF) model in a test set of the NGT and T2D subjects. We evaluated its performance on unseen samples from the same groups and scored the predictive power in a receiver operator characteristic (ROC) analysis. The RF model generates a variable importance score for each species and MGC representing the predictive power. The importance score was used to rank species and MGCs, and the top most important ones were used in a model for predicting T2D. The discriminatory power of species and MGCs was calculated as the area under the ROC curve (AUC) (FIG. 3 a). T2D was predicted more accurately with MGCs (highest AUC=0.83, 50 MGCs) than with microbial species (highest AUC=0.71, 238 species) (FIG. 3 a, Table 3). When body mass index (BMI), waist-to-hip ratio (WHR) and waist circumference (WC) were used for predicting T2D we obtained a maximum AUC of 0.70 for WC (AUC for BMI=0.58; AUC for WHR=0.60), thus showing that the composition of the microbiota determined by MGCs correlates better with T2D than these known risk factors (22). Importantly, the T2D score obtained based on MGC clusters is similar to other published scores that combine several known risk factors for diabetes development (e.g. the FINDRISC score, validated in several countries (7)).” (para. [0122]).
Backhed teaches assessing the risk of the person to be in a prediabetes diseased state using the classification model and the quantified abundance of the sensory protein in the metagenomic sample of the person with “The present invention further provides a method for identifying an individual having or at risk of having or suspected of having/developing T2D comprising obtaining a gut microbial sample from said individual and determining the levels or abundance in said sample of at least the top 5, 10, 15, 20, 25 or 30 of the most predictive MGCs or bacterial species identified by the methods or models of the present invention. It should be noted that once the most predictive MGCs have been identified then the bacterial species (or in some cases orders) corresponding to these can readily be identified (e.g. by using reference genomes such as those at NCBI). Such bacterial species (and orders) are indicated in FIG. 3 b and it can be noted that they are different from the species identified in FIG. 3 c (i.e. in the analysis not involving the use of MGCs) and these species can conveniently be used for diagnosis of T2D.” (para. [0085]) and with “Once the levels or abundance of the species have been analysed as above, it is then determined whether the individual has T2D or is normal (healthy, NGT) by appropriate techniques, e.g. by comparison to levels in samples from patients known to have T2D or from healthy/control individuals. The methods or models of the invention could be used for this assignation/stratification/classification of patients to a normal group or a T2D group. In addition, when the population group is appropriate, information as to the type of correlation associated with various species (i.e. whether or not they are associated with T2D or normal/NGT groups) can be obtained from the information provided in the attached Examples and Figures (for example FIG. 3 b or 9 b).” (Para. [0092]).
Backhed teaches wherein the assessment results in categorization of the person either in a low risk or a high risk of prediabetes diseased state based on a predefined criteria with “The present invention relates to the identification of a person having risk for developing type 2 diabetes (T2D) by determining the presence or absence of specific genes, gene clusters, genera or species of bacteria in the person's gastrointestinal microbiota. More specifically the invention relates to a model to identify an individual having or at risk of developing type 2 diabetes (T2D) using metagenomic clusters (MGCs), wherein said model is characterized by using different metagenomic clusters for different population groups. Also provided is the use of such a model in the identification of a person having risk for developing type 2 diabetes (T2D).” (Abstract).
Lima teaches wherein sequences corresponding to the annotated sensory proteins are used as the database and rest of obtained bacterial protein sequences are used as a query with “In brief, the system works as follows (Figure 3): after a complete genome is deposited in DDBJ/EMBL/GenBank (24) entries are produced containing the original annotation that was provided by the submitter, plus, in some cases, automatically added additional annotation. These entries are stored in UniProtKB/TrEMBL, the unreviewed section of UniProtKB. All microbial and plastid protein sequences in UniProtKB/TrEMBL are run daily against the HAMAP profile collection and family members are identified. Matches with a score above the cutoff are annotated using the annotation templates and are integrated into UniProtKB/Swiss-Prot; problematic proteins (for example, sequences having unusual length, missing conserved amino acid residues or having aberrant N termini) generate warnings and are channeled to manual review and annotation.” (page D475, col. 1, para. 5). The recited “query” is interpreted to correspond to “manual review and annotation” as taught by Lima.
Loomba teaches capturing an importance of each of the features included in cross- validation models in terms of GINI index; selecting a predefined number of most 'important' features based on GINI index values from each of the 100 cross-validation RF models to obtain a feature sub-set with “We developed a series of steps to train a Random Forest with the best overall accuracy of classification, which we report as AUC. We trained 300 forests, containing 1001 trees each, with the relative genome abundances of species that passed abundance and prevalence filtering as previously described. In addition, the Shannon Diversity Index and richness of each sample, and the age, BMI, gender, and race of each patient were also included in the training set. Due to the small number of patients in Group 2 in comparison to Group 1, training was done with stratified sampling in which features from an equal number of samples from each group were randomly sampled and used to train each tree. A trained forest produces a variable importance list based on mean decrease in Gini index. For our dataset the variable importance list is a list of species, sample indices, or patient measurements that contributed most to the correct classification or the correct group assignment of every sample. The species importance list from the forest with the highest AUC is selected for Iterative Feature Elimination, which is described next.” (page e7, para. 7).
Chen teaches Cross-validation method with “The ten-fold cross-validation method is a popular cross-validation approach that is often used to examine the performance of prediction methods. In this method, a given dataset is equally and randomly divided into ten parts. The samples in each part are selected as testing samples to test the classifier that is trained by samples in the other nine parts. Thus, each sample is tested only once. Herein, it was adopted to examine the performance of different prediction models.” (Page 3, para. 6).
Chen teaches Matthews’s correlation coefficient with “Accuracy measurement. As mentioned in Section “Materials”, all samples were classified into three classes. To evaluate the performance of a certain prediction model, we can calculate the accuracies for three classes and overall prediction accuracy. However, none of them can accurately evaluate the performance of the prediction model because the dataset was an imbalance dataset, in which the European samples were more than five times as many as the American samples. For a two-class classification problem, the Matthews’s correlation coefficient (MCC)22 is always used to evaluate the performance of a prediction model because it is a balanced measure even if the classes are of very different sizes. In 2004, Gorodkin23 proposed the MCC for multiclass case. Here, we employed it to evaluate the performance of various prediction models.” (Page 3, para. 7).
Stritzker teaches wherein the plurality of Healthy Therapeutic Markers (HTMs) comprises one or more of Oceanithermus profundus, Pseudoxanthomonas spadix, Rhodothermus marinus, Thermaerobacter marianensis and wherein the Disease Markers (DMs) comprise of Acholeplasma palmae with “Bacteria can also be used in the methods provided herein. Any of a variety of bacteria possessing the desired characteristics can be used. In one embodiment, aerobic bacteria can be used. In another embodiment, anaerobic bacteria can be used. Exemplary bacteria provided herein include, for example, …, Acholeplasma, …, Obesumbacterium, Oceanibulbus, Oceanicaulis, Oceanicola, Oceanimonas, Oceanisphaera, Oceanithermus, Oceanobacillus, Oceanobacter, Oceanospirillum, Ochrobactrum, Octadecabacter, …, Pseudaminobacter, Pseudoalteromonas, Pseudoamycolata, Pseudobutyrivibrio, Pseudocaedibacter, Pseudoclavibacter, Pseudomonas, Pseudonocardia, Pseudoramibacter, Pseudorhodobacter, Pseudospirillum, Pseudovibrio, Pseudoxanthomonas, Psychrobacter, Psychroflexus, Psychromonas, Psychroserpens, Pusillimonas, …, Rhodanobacter, Rhodobaca, Rhodobacter, Rhodobium, Rhodoblastus, Rhodocista, Rhodococcus, Rhodocyclus, Rhodoferax, Rhodoglobus, Rhodomicrobium, Rhodopila, Rhodopirellula, Rhodoplanes, Rhodopseudomonas, Rhodospira, Rhodospirillum, Rhodothalassium, Rhodothermus, Rhodovarius, Rhodovibrio, Rhodovulum, …Thermacetogenium, Thermaerobacter, Thermanaeromonas, Thermanaerovibrio, Thermicanus, Thermincola, Thermithiobacillus, Thermoactinomyces, Thermoanaerobacter, Thermoanaerobacterium, Thermoanaerobium, Thermobacillus, Thermobacteroides, Thermobifida, Thermobispora, Thermobrachium, Thermochromatium, Thermocrinis, Thermocrispum, Thermodesulfatator, Thermodesulfobacterium, Thermodesulfobium, Thermodesulforhabdus, Thermodesulfovibrio, Thermoflavimicrobium, Thermohalobacter, Thermohydrogenium, Thermoleophilum, Thermomicrobium, Thermomonas, Thermomonospora, Thermonema, Thermopolyspora, Thermosinus, Thermosipho, Thermosyntropha, Thermoterrabacterium, Thermothrix, Thermotoga, Thermovenabulum, Thermovibrio,...” (para. [0489]).
It would have been prima facia obvious to combine the teachings of Bajaj, Backhed, Loomba and Chen to arrive at the claimed invention. Backhed’s model has been shown to be able to identify the risk groups with 80% accuracy or with an area under the ROC curve (ROC AUC) of up to or greater than 0.83 (Para. [0018]). A person of ordinary skill in the art would have been motivated to modify the method of Bajaj by including a random forest classifier to classify diabetes risk based on gut microbes as taught by Backhed to accurately identify the risk groups. A person of ordinary skill in the art would have also been motivated to modify the method of Bajaj by evaluating the model’s performance as taught by Backhed to effectively ensure that model is accurate. A person of ordinary skill in the art would have been motivated to modify the method of Bajaj to include the GINI index as taught by Loomba to generate a variable importance list (page e7, para. 7). A person of ordinary skill in the art would have also been motivated to modify the method of Bajaj by including MCC as taught by Chen to evaluate the performance of a prediction model because it is a balanced measure even if the classes are of very different sizes (Page 3, para. 7). Furthermore, there would have been a reasonable expectation of success, since Bajaj, Backhed, Loomba and Chen teach methods that pertain to the analysis of gut microbes and disease.
It would have been prima facia obvious to combine the teachings of Bajaj, Backhed, Lima, Loomba and Chen to arrive at the claimed invention. A person of ordinary skill in the art would have been motivated to modify the method of Bajaj by storing sequences corresponding to the annotated proteins in the database and manually review protein sequences with no corresponding annotated protein as taught by Lima for the purpose of detecting sequence errors to improve the data stored in the database. Furthermore, there would have been a reasonable expectation of success, since Bajaj, Backhed, Lima, Loomba and Chen teach methods that pertain to the analysis of microbes.
It would have been prima facia obvious to combine the teachings of Bajaj and Stritzker to arrive at the claimed invention. Stritzker provides methods for detecting a microorganism or cell in a subject and methods for detecting, imaging or diagnosing a site, disease, disorder or condition in a subject using microorganisms or cells (Abstract). Strizker also teaches the use of microorganisms or cells (e.g. Nissle) for the formulation of compositions for use in the methods provided herein for detecting and/or treating a site of proliferation or a proliferative condition, such as a tumor, tumor tissue, cancer or metastasis (para. [0016]). A person of ordinary skill in the art would have been motivated to modify the method of Bajaj by including Healthy Therapeutic Markers (HTMs) of Oceanithermus profundus, Pseudoxanthomonas spadix, Rhodothermus marinus, Thermaerobacter marianensis and the Disease Markers (DMs) of Acholeplasma palmae as taught by Stritzker to accurately identify individuals with a risk of prediabetes. Furthermore, there would have been a reasonable expectation of success, since Bajaj and Stritzker teach methods that pertain to the analysis of gut microbes and disease.
Regarding claim 3, Bajaj teaches wherein, the therapeutic construct comprises one or more of: a plurality of Healthy Therapeutic Markers (HTMs), wherein the plurality of Healthy Therapeutic Markers is non-pathogenic, species and strains belonging to same genus of the HTMs, wherein the species and strains are non-pathogenic, a plurality of organisms having more than 90 percent identity and coverage over the genome of HTMs, wherein the plurality of organisms are non-pathogenic, one or more organisms which boost the population of HTMs, wherein the one or more organisms are non-pathogenic, one or more of a natural or synthetically derived compounds which boost the population of HTMs, wherein the natural or synthetically derived compounds are non-toxic, or one or more of a natural or synthetically derived compounds which targets the Disease Markers (DMs), wherein the natural or synthetically derived compounds are non-toxic and do not cause any adverse effect with “In some embodiments, the patient may already exhibit overtly one or more symptoms of a disease of interest. But, by using the methods of the invention, it is possible to ascertain whether or not a likely cause of the disease symptom(s) is gut microflora identity (composition of the microbiome) and/or distribution, and hence whether or not gut microflora are a likely target for successful treatment. In other embodiments, a subject may be asymptomatic with respect to a disease or condition of interest, but for some reason, may be deemed susceptible to developing the disease or condition, and the methods of the invention provide a way to predict whether or not this is likely to occur. In some embodiments, the identification of particular microflora (e.g. of particular phyla, genera or species of microbe) may allow targeted therapies directed against the microbe or microbes which are undesirable, and/or therapies which increase the amount of desirable gut microflora, e.g. those which compete with the undesirable microbes, and/or which supply activities or produce substances which are beneficial, especially with respect to the disease or condition of interest.” (para. [0031]). The recited “Healthy Therapeutic Markers” corresponds to “desirable gut microflora, e.g. those which compete with the undesirable microbes, and/or which supply activities or produce substances which are beneficial” as taught by Bajaj.
Bajaj does not explicitly teach wherein the sequence alignment is performed using one or more of Basic Local Alignment Search Tool (BLAST), BLAST-like alignment tool (BLAT), DIAMOND alignment tool, RAPSearch tool, Burrows-Wheeler Aligner (BWA), Bowtie or through the use of clustering algorithms compriseing BLASTCLUST, CLUSTALW, VSEARCH or heuristic techniques of identifying sequence similarity of claim 6 and wherein the plurality of public repositories comprises one or more of NCBI database, Protein Data Bank, KEGG database, PFAM database or EggNOG of claim 7. However, Backhed teaches these limitations.
Regarding claim 6, Backhed teaches wherein the sequence alignment is performed using one or more of Basic Local Alignment Search Tool (BLAST), BLAST-like alignment tool (BLAT), DIAMOND alignment tool, RAPSearch tool, Burrows-Wheeler Aligner (BWA), Bowtie or through the use of clustering algorithms compriseing BLASTCLUST, CLUSTALW, VSEARCH or heuristic techniques of identifying sequence similarity with “To determine the composition of the gut microbiota, we aligned filtered Illumina reads to 2382 non-redundant reference genomes obtained from the NCBI and HMP databases (hmpdacc.org). The majority of the aligned reads (38±9.7% (SD)) belonged to the bacterial phyla Firmicutes and Bacteroidetes, each representing 67±12% (SD) and 19±12% (SD) of the microbiota (FIG. 4 a).” (para. [0113]) and with “To determine the composition of the gut microbiota, the filtered Illumina reads are aligned to multiple non-redundant reference genomes obtained from for example the NCBI and HMP databases (hmpdacc.org). The most abundant genera, species and genomes in the cohort are calculated and compared between the subgroups.” (para. [0055]).
Regarding claim 7, Backhed teaches, wherein the plurality of public repositories comprises one or more of NCBI database, Protein Data Bank, KEGG database, PFAM database or EggNOG with “To characterize microbial functions we annotated all the genes of our catalogue to the KEGG database (version 59). We then used the reporter feature algorithm (25, 26) in combination with the KEGG metabolic network, pathway annotations and the information about relative gene abundance to identify reporter pathways (i.e. pathways with significantly differentially abundant KOs) that were associated with T2D and NGT status. We found that, despite having an equivalent number of genes (discussed above, data not shown), NGT and T2D communities had different functional composition and several reporter pathways were differentially abundant in T2D and NGT women. The pathways that showed the highest scores for enrichment in T2D metagenomes included KOs for starch and glucose/sucrose metabolism, fructose and mannose metabolism, and ABC transporters for amino acids, ions and simple sugars. In particular, 39 out of 46 KOs for starch and glucose/sucrose metabolism and 37 out of 49 KOs for fructose and mannose metabolism were more abundant in T2D compared to NGT metagenomes. For ABC transporters, 123 out of 174 KOs were more abundant inT2D metagenomes compared to NGT. These results are in agreement with previous studies showing an increase in microbial functions for energy metabolism and harvest in the obese microbiome (27, 28). Other metabolic pathways containing KOs enriched in women with T2D included glycerolipid metabolism and fatty acid biosynthesis. Finally, also enriched in T2D were the pathways for cysteine and methionine metabolism, which is related to glutathione synthesis and may be important for response to oxidative stress. Similarly, membrane transporters for sugars and branched-chain amino acids as well as genes related to oxidative stress resistance were also enriched in the metagenome of Chinese diabetic patients (11). Microbial functions enriched in NGT women were related to flagellar assembly and riboflavin metabolism. Interestingly, the metagenome of healthy individuals in the Chinese cohort was also enriched in functions related to flagellar assembly, and these functions belonged to bacteria in the Roseburia, Butyrivibrio and Eubacterium genera (11), while in our study they correlated to enterobacteria and Roseburia.” (para. [0125]) and with “To determine the composition of the gut microbiota, the filtered Illumina reads are aligned to multiple non-redundant reference genomes obtained from for example the NCBI and HMP databases (hmpdacc.org). The most abundant genera, species and genomes in the cohort are calculated and compared between the subgroups.” (para. [0055]).
It would have been prima facia obvious to combine the teachings of Bajaj and Backhed to arrive at the claimed invention. Backhed’s model has been shown to be able to identify the risk groups with 80% accuracy or with an area under the ROC curve (ROC AUC) of up to or greater than 0.83 (Para. [0018]). A person of ordinary skill in the art would have been motivated to modify the method of Bajaj by including the sequence alignment as taught by Backhed to determine the composition of gut microbiota (para. [0113]). A person of ordinary skill in the art would have also been motivated to modify the method of Bajaj by including public repositories, such as NCBI database or KEGG database as taught by Backhed to obtain multiple non-redundant reference genomes from the NCBI and HMP databases for read alignments to determine the composition of gut microbiota (para. [0055]). Furthermore, there would have been a reasonable expectation of success, since Bajaj and Backhed teach methods that pertain to the analysis of gut microbes and disease.
Response to Arguments under 35 USC 103, pages 27-43
Applicant's arguments filed 01/12/2026 have been fully considered but they are not persuasive.
Claims 1, 3 and 6 are amended. Claims 2, 4-5 and 8-11 are cancelled.
It is noted that arguments are based on amended claims.
Applicant argues that Bajaj, Backhed, Loomba, Chen and Stritzker do not teach the limitations of claim 1.
Applicant asserts that Bajaj fails to disclose the amended limitation of claim 1, "creating a database of sensory protein sequences of a plurality of organisms, wherein the database of sensory protein sequences comprises information pertaining to a sensory protein of all fully or partially sequenced bacterial genomes obtained from a plurality of public repositories, wherein creation of the database of sensory protein sequences is a one-time process and created before a test microbiome sample from the person is provided for diagnosis and therapeutic purposes." Applicant states that Bajaj merely teaches recording and storing the characteristics of the reference microfloral signatures in a database such as the Ribosomal Database versions 8 and 10, Greengenes, and Genbank.
In response, Applicant’s remarks are not persuasive. Bajaj teaches reference signatures are generally recorded (stored, compiled, etc.) in an electronic computerized catalog, library, database, etc. (para. [0047]) as discussed in the 103 rejection section above. It is prima facia obvious that a database is created in order to be able to record or store data in a database. Also, the limitation of wherein creation of the database of sensory protein sequences is a one-time process and created before a test microbiome sample from the person is provided for diagnosis and therapeutic purposes is not an active step and has no patentable weight (See MPEP 211.05). Additionally, the selection of any order of performing process steps is prima facie obvious in the absence of new or unexpected results (see MPEP 2144.04(C)).
Applicant asserts that Bajaj teaches sequence alignment, but does not teach sequences corresponding to the annotated sensory proteins are used as the database and rest of obtained bacterial protein sequences are used as a query, as amended in claim 1.
In response, as discussed in the 112(b) rejection section above, the recitation of “…sequences corresponding to the annotated sensory proteins are used as the database…” in claim 1 is unclear. It is unclear what is meant by used as the database and whether this is the generation of a new database or whether it is the same database as the database in the creating step. For compact prosecution, it is interpreted as saving the data in the database created in the creating step.
As discussed in the 103 rejection section above, Lima teaches wherein sequences corresponding to the annotated sensory proteins are used as the database and rest of obtained bacterial protein sequences are used as a query with “In brief, the system works as follows (Figure 3): after a complete genome is deposited in DDBJ/EMBL/GenBank (24) entries are produced containing the original annotation that was provided by the submitter, plus, in some cases, automatically added additional annotation. These entries are stored in UniProtKB/TrEMBL, the unreviewed section of UniProtKB. All microbial and plastid protein sequences in UniProtKB/TrEMBL are run daily against the HAMAP profile collection and family members are identified. Matches with a score above the cutoff are annotated using the annotation templates and are integrated into UniProtKB/Swiss-Prot; problematic proteins (for example, sequences having unusual length, missing conserved amino acid residues or having aberrant N termini) generate warnings and are channeled to manual review and annotation.” (page D475, col. 1, para. 5). The recited “query” is interpreted to correspond to “manual review and annotation” as taught by Lima.
Applicant asserts that Backhed fails to disclose the amended limitation of claim 1 of "applying, via one or more hardware processors, a random forest classifier on the generated sensory protein abundance profiles of case-control samples to generate a classification model, wherein generation of the classification model is a one-time process and generated before the test microbiome sample from the person is provided for the diagnosis and therapeutic purposes"
In response, Applicant’s remarks are not persuasive. Backhed teaches using a Random Forest (RF) model identify diabetes status (para. [0056]) as discussed in the 103 rejection section above. Also, the limitation of wherein generation of the classification model is a one-time process and generated before the test microbiome sample from the person is provided for the diagnosis and therapeutic purposes is not an active step and has no patentable weight (See MPEP 211.05). Additionally, the selection of any order of performing process steps is prima facie obvious in the absence of new or unexpected results (see MPEP 2144.04(C)).
Applicant asserts that Bajaj does not disclose methods for predicting risk of prediabetes by analyzing a microbiome sample utilizing sensory proteins abundance as disclosed in the claimed invention and about the usage of sensory protein and calculation of abundance of a sensory protein in the sequenced metagenomic reads to diagnose the presence or assess the risk/ predisposition of a disease. Applicant also asserts that Bajaj does not teach about specific implementations to calculate the abundance of sensory protein in the sequenced metagenomic reads, as amended in claim 1. Applicant states that the methodology adopted for computation of the 'sensory protein abundance' to ascertain risk categories, which is an integral part of the present invention, is entirely novel and is not obviously derivable from Bajaj.
In response, Applicant’s remarks are not persuasive. As indicated in the 112(a) and 112(b) section above claim 1 recites “…quantifying abundance of a sensory protein from the sequenced metagenomic reads using the database of sensory protein sequences (page 4, Lines 9-11) …performing a sequence alignment with the sequences in the created sensory protein sequence database as query against the sequenced metagenomic reads (page 4, Lines 12-13) … computing cumulative matches of the sequenced metagenomic reads… (page 4, Line 17).” It is unclear how sequence alignment is performed between the protein sequences and metagenomic reads, since metagenomic reads are DNA sequences. As indicated in the specification (para. 10-11), the extracted DNA is then sequenced to get sequenced metagenomic reads. Protein sequences cannot be directly aligned with metagenomic reads. Therefore, it is also unclear how sensory protein abundance is calculated.
However, as discussed in the 103 rejection section above, Bajaj teaches analyzing the protein content of a biological sample with “In yet other embodiments of the invention, the gut signature of a subject may be, or may include, results obtained by analyzing the protein content of a biological sample (e.g. a gut sample), of a subject. The results may include the identity of the proteins, the presence or absence of selected proteins, the relative abundance of the proteins (e.g. compared to suitable controls), etc. The proteins may be associated with (e.g. characteristic of) one or more bacterial (or other microfloral) taxa of interest. Exemplary proteins that may be included in such a gut proteome signature include but are not limited to those which are known to those of skill in the art.” [0049]. This teaching is interpreted to correspond to calculating the abundance of sensory protein. Bajaj also teaches determining a patient's risk of developing a disease or condition (abstract) and Backhed is relied on for teaching assessing the person either in a low risk or a high risk of prediabetes diseased state with “The present invention relates to the identification of a person having risk for developing type 2 diabetes (T2D) by determining the presence or absence of specific genes, gene clusters, genera or species of bacteria in the person's gastrointestinal microbiota. More specifically the invention relates to a model to identify an individual having or at risk of developing type 2 diabetes (T2D) using metagenomic clusters (MGCs), wherein said model is characterized by using different metagenomic clusters for different population groups. Also provided is the use of such a model in the identification of a person having risk for developing type 2 diabetes (T2D).” (Abstract).
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.K./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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