DETAILED ACTION
Notice of 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 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.
Withdrawal of Objections and Rejections
Applicant's response, filed 01/20/2026, has been fully considered.
In view of the amendment and remarks from 01/20/2026, the objection to the claims and the rejection of the following claims are withdrawn:
claims 13-14 under 35 USC § 112(a);
claims 1-14 under 35 USC § 112(b);
The following rejections and/or objections are either maintained or newly applied for claims 1, 6-7, 9-12 and 15. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Non-Final Rejection of 10/21/2025.
Status of the Claims
Claims 2-5, 8 and 13-14 are canceled.
Claims 1, 6-7, 9-12 and 15 are pending.
Claim 11 is objected to.
Claims 1, 6-7, 9-12 and 15 are rejected.
Priority
This US Application 17/634,949 (02/11/2022) is a 371 of PCT/IB2020/057585 (08/12/2020) and claims benefit of Foreign Application IN 201921032793 (08/13/2019) as reflected in the filing receipt mailed on June. 08, 2022. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1, 6-7, 9-12 and 15 is 08/13/2019.
Claim objections
Claim 11 is objected to because of the following informality: the "and" conjunction is placed incorrectly within the claim elements. As it appears in the claim, the "and" is placed between the 10th and 11th claim element; but it should be placed between the penultimate and the last claim element. Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1, 6-7, 9-12 and 15 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. Any newly recited portions are necessitated by claim amendment. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
Claim 1 recites “less characterized” which is indefinite because there are terms of relative or vague degree or form of association, neither defined in the specification ([043]) nor having a well-known and sufficiently particular definition in the art and in the instant context. The disclosure at [043] is not interpreted as a definition since it does not define the exact definition for a low, medium or high risk. Although claims are interpreted in light of the specification, examples from the specification are not imported into the claims as limitations absent a clearly limiting definition in the specification. MPEP 2173.05(b) pertains.
Claim 1 recites "wherein the therapeutic construct comprises one or more …, wherein the therapeutic construct comprises one or more of: …" which is unclear because there are two different wherein clauses about what the "therapeutic construct comprises." It is unclear if all therapeutic construct elements are supposed to make up one list or not. If there are separate lists, the scope for each list is unclear. The rejection may be overcome by amending the claim to clarify the metes and bounds of the limitation
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 112(b)
The Remarks of 01/20/2026 have been fully considered but are not persuasive for the reasons below:
Regarding the amendments related to the previously recited "poorly annotated" and "low/medium/high risk", these terms are still identified as relative terms since there is no exact definition of what classify the "less characterized" (claim 1) and "low/medium/high risk" (claims 1 and 15). Both terms are terms of relative or vague degree or form of association. Claim 15 does not solve the issue for "low/medium/high risk." It is still unclear what defines a less characterized sensory protein sequence or what defines a risk level to be considered low, medium or high. MPEP 2173.05(b) pertains.
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, 6-7, 9-12 and 15 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). Any newly recited portions are necessitated by claim amendment.
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 1, 6-7, 9-12 and 15) which falls within one of the categories of statutory subject matter.
[Step 1: claims 1, 6-7, 9-12 and 15: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) as well as a law of nature or a natural phenomenon are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "applying, via the one or more hardware processors, a random forest classifier on the generated sensory protein abundance profiles of the set of control versus adenoma samples, the set of control versus carcinoma samples, and the set of adenoma versus carcinoma samples to generate their respective classification models" (independent claim 1);
• "quantifying, via the one or more hardware processors, the abundance of a sensory protein from the sequenced metagenomic reads using the database of sensory protein sequences" (independent claim 1);
• "applying a Random Forest (RF) approach on the sensory protein abundance profiles of sequenced metagenomic reads" (claim 11);
• "performing 10 replicates on 10-fold cross-validation on the training set to build 100 cross-validation RF models" (claim 11);
• "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" (claim 11);
• "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" (claim 11);
• "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 a training model and the confidence probability of the prediction to be 'case'(control versus adenoma: case adenoma; control versus carcinoma: case carcinoma; adenoma versus carcinoma: case carcinoma) were accounted" (claim 11);
• "calculating the abundance of the sensory protein" (claim 12);
• "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" (claim 12);
• " 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" (claim 12); and
• "calculating the sensory protein abundance using one of the following: calculating ratio of the count of sensors to the 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" (claim 12).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined to each cover performance either in the mind and/or by mathematical operation. Without further detail as to the methodology involved in "computing and calculating parameters to arrive at the sensory protein abundance values", under the BRI, one may simply, for example, use pen and paper to perform mathematical steps to arrive at the described steps. Further support for the mathematical techniques used in the claims is provided in the specification at [030] which discloses that computation of sensory protein abundance can be performed by calculation of parameters. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "identifying all annotated sensory proteins from the extracted data using a set of keyword searches" (independent claim 1);
• "performing a sequence alignment to identify a set of less characterized sensory protein sequences" (independent claim 1);
• "filtering the results of the sequence alignment based on 95% identity, 95% coverage and an e-value of cut-off 1.0*e-5 (0.00001) to identify a set of additional sensory protein sequences" (independent claim 1);
• "collating the sensory protein sequences and the sequences identified through sequence alignment to create the database of sensory protein sequences" (independent claim 1);
• "generating … sensory protein abundance profiles of a set of control versus adenoma samples, a set of control versus carcinoma samples, and a set of adenoma versus carcinoma samples obtained from publicly available data" (independent claim 1);
• "assessing, via the one or more hardware processors, the risk of the person to be in the CRC diseased state using the respective classification models and the computed abundance of the sensory protein in the metagenomic sample of the person, wherein the assessment results in the categorization of the person either in a low risk, a medium risk or a high risk of colorectal cancer diseased state based on a predefined criteria" (independent claim 1);
• "selecting a random set of sequenced metagenomic reads comprising 90% of fecal/ stool microbiome samples as a training set and comprising remaining 10% as a test set" (claim 11);
• "capturing an importance of each of the features included in cross-validation models in terms of GINI index" (claim 11);
• "selecting a predefined number of 'important' features based on GINI index values from each of the 100 cross-validation RF models to obtain a feature subset; wherein value of the predefined number vary from 2 to 'N', wherein 'N' is total number of features, wherein each feature in the sub-set of features, obtained by choosing the predefined number of 'important' features from each of the 100 cross-validation RF models" (claim 11);
• "ranking each of the features in the feature sub-set, on the basis of the sum of their GINI index values" (claim 11);
• "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 1.0*e-5 (0.00001) are considered as correct matches" (claim 12).
Under the BRI, the recited limitations are mental processes because a human mind is sufficiently capable of evaluate risk based on data identified, select data according to a criteria, gather identified data to create a database; make a random data selection from a list of available data; and evaluate the importance of data. Under BRI, the recited “extracting a data from the plurality of public repositories," "identifying all annotated sensory proteins from the extracted data," and "performing a sequence alignment" constitutes identifying data from a public database looking for keyword searches and comparing two sequences. Under BRI, the recited “generating, via the one or more hardware processors, sensory protein abundance profiles of a set of control versus adenoma samples, a set of control versus carcinoma samples, and a set of adenoma versus carcinoma samples obtained from publicly available data” constitutes organization/collation of data; in which a human mind could write down the number of sequences present in a set of a samples with pen and paper.
Dependent claims 6, 9 and 15 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claims 1 and 15 recite further details about the risk assessing step; and claim 9 recites further limits the sequence alignment (i.e. initially identified as a mental process) to being performed by an algorithm (i.e. constituting a mathematical concept – hence a judicial exception)
Furthermore, the instant claims recite a natural correlation by correlating the measurement of an amount of a protein naturally found in the body with the risk for a disease. (see MPEP 2106.04(b).I).
[Step 2A Prong One: claims 1, 6-7, 9-12 and 15: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claim 1 recites additional elements that are not abstract ideas:
• "one or more hardware processors" (independent claim 1);
• "creating, via one or more hardware processors, a database of sensory protein sequences of a plurality of organisms, wherein the database of sensory protein sequences comprises information pertaining to the sensory proteins of all fully or partially sequenced bacterial genomes obtained from a plurality of public repositories" (independent claim 1);
• "extracting data from the plurality of public repositories" (independent claim 1);
• "collecting a microbiome sample from a body site of the person for the assessment of the risk of CRC, wherein the microbiome sample comprising microbial cells" (independent claim 1);
• "extracting DNA from the microbial cells" (independent claim 1);
• "sequencing, via a sequencer, using the extracted DNA to get sequenced metagenomic reads" (independent claim 1);
• "administrating a therapeutic construct to the person depending on the risk of the colorectal cancer, wherein the therapeutic construct comprises one or more non- pathogenic Healthy Therapeutic Markers (HTMs), a plurality of antibiotic drugs targeted against Disease Markers, prebiotics, probiotics, syn biotics, post biotics or fecal microbiome transplant to help the person's gut microbiome to attain a healthy equilibrium, wherein the plurality of Healthy Therapeutic Markers (HTMs) comprises one or more of Candidatus saccharibacteria, Fibrobacter succinogenes, Haliangium ochraceum, Calothrix sp., Lactobacillus sanfranciscensis, Methanocaldococcus infernus, Nostoc punctiforme, Planctomyces limnophilus, Sphingobium chlorophenolicum, Stigmatella aurantiaca, or Veillonella parvula, and administered either alone or in concoction for therapeutic purposes, wherein the Disease Marker (DM) comprises Solitalea canadensis, wherein the therapeutic construct comprises one or more of: 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 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 natural or synthetically derived compounds which target Disease Markers (DMs), wherein the natural or synthetically derived compounds are non-toxic and do not cause any adverse effects" (independent claim 1);
Dependent claim 7 recites further details about the sample collected. Dependent claim 10 recite further details about the public repositories from which data is extracted.
Considerations under Step 2A, Prong Two
The recited limitations in claim 1 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. The instant claims state nothing more than that a generic computer performs the functions that constitute the abstract idea (MPEP 2106.05(f)).
Limitations of claims 1, 6-7, 9-12 and 15 are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Claims reciting “creating a database, “extracting data from public repositories”, “collecting a sample”; and “performing data replicates” merely serve as necessary data gathering activities.
Claims reciting "sequencer”, “extracting DNA”, “sequencing” and “performing a sequencing alignment” read on detecting DNA in a patient sample, being an insignificant extra-solution activity since this limitation merely serve to gather data that is utilized as input for the judicial exception. See MPEP 2106.05(g) and MPEP 2106.04(d).
Claims reciting "administrating a therapeutic construct to the person depending on the risk of the colorectal cancer" are not sufficient to integrate that abstract idea into a practical application. There is no evidence linking specific treatments to specific risks, thus it is not clear that the treatment has a more than nominal relationship for all risk categories. Without that affirmative recitation linking specific treatments to specific risks, the particular treatment consideration does not apply (see MPEP 2106.04(d)(2)).
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1, 6-7, 9-12 and 15: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claim 1 recites a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Claims directed to “creating a database, “extracting data from public repositories”, “collecting a sample from a person”; recite steps known in the art as conventional – Hofmann et. al. “Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders” Int. J. Mol. Sci. 16:29179–29206 (2015) (pg. 29179 para. 1 - publicly available data; pg. 29199 para. 4 – creating a database; pg. 29194 Fig. 2 – collecting a sample from a person).
Claims directed to “microbiome sequencing analysis”; recite steps known in the art as conventional – Di Bella et. al. "High throughput sequencing methods and analysis for microbiome research." Journal of microbiological methods 95(3):401-414 (2013) – pg. 403 Table 1).
As it appears in the claims, there is no evidence linking specific treatments to specific risks, thus it is not clear that the treatment has a more than nominal relationship for all risk categories.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1, 6-7, 9-12 and 15: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 101
The Remarks of 01/20/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 19 para. 2
Step 2A - Prong 2: the claim recited limitations do not recite additional elements that integrate the judicial exception into a practical application by 'administrating a therapeutic construct to the person depending on the risk of the colorectal cancer, wherein the therapeutic construct comprises one or more non-pathogenic Healthy Therapeutic Markers (HTMs), a plurality of antibiotic drugs targeted against Disease Markers, pre biotics, probiotics, symbiotics, post biotics or fecal microbiome transplant to help the person's gut microbiome to attain a healthy equilibrium]. … Applicant's claimed invention provides a method and system for assessing the risk of colorectal cancer in a person. The system 100 is configured to assess individuals to check the risk of presence of colorectal cancer (CRC) and/or adenomatous (colonic/ rectal) polyps, by quantifying the abundance of sensory proteins in their gut microbiome. The system 100 further categorizes the person into one of healthy, adenoma and cancerous categories based on the nature and abundance of sensory proteins in the gut microbiome. The system 100 further describes … As stated in paragraphs [041]-[043] of the specification, the administration module 122 is configured to provide/ administer a therapeutic construct to the person depending on the risk of the colorectal cancer. The administration module 122 uses at least one of a consortium/ construct of healthy microbes, antibiotic drugs and pre-/ pro-/ syn-/ post-biotics or fecal microbiome transplant that would help the patient's gut microbiome to attain a healthy equilibrium without any adverse health effects. The therapy may be provided in the form of anyone (or a combination) of the known routes of administrations like intravenous solution, sprays, patches, band-aids, pills or syrup. The therapeutics is suggested as a consortium of microbes based on their (inverse) correlation with the disease microbiome which can contribute to the therapeutic treatment for prediabetes by modulating the disease microbiome towards healthy equilibrium. Different implementations to identify the suitable therapeutic candidates are as following ...
It is respectfully submitted that this is not persuasive because the argued "administering step" consist of administrating a therapeutic construct to the person depending on the risk of the colorectal cancer which is not sufficient to integrate that abstract idea into a practical application. There is no evidence linking specific treatments to specific risks, thus it is not clear that the treatment has a more than nominal relationship for all risk categories. Without that affirmative recitation linking specific treatments to specific risks, the particular treatment consideration does not apply (see MPEP 2106.04(d)(2)).
Applicant asserts starting in pg. 21 para. 2
Referring to Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68 (Fed. Cir. 2011), the Federal Circuit concludes that "the disclosed invention are directed to a specific, tangible application" because they include the physical step of immunization on the determined schedule. … Classen argues that the immunization step is conducted after selection of a lower risk schedule, as in the '139 and '739 claims, or that immunization produces information about immunization effects, as in the '283 claims. Thus Classen states that its claims are not directed to an abstract idea like the commodity hedging method in Bilski v. Kappos." In similar lines of the Federal Circuit decision (physical step), the claimed subject matter provides a physical step of administering a therapeutic construct to the person depending on the risk of the colorectal cancer. The system is configured to assess individuals to check the risk of presence of colorectal cancer (CRC) and/or adenomatous (colonic/ rectal) polyps, by quantifying the abundance of sensory proteins in their gut microbiome. The system further categorizes the person into one of healthy, adenoma and cancerous categories based on the nature and abundance of sensory proteins in the gut microbiome. The system further describes microbiota based therapeutics for treatment of the person with colorectal adenoma and/or cancer through administration of at least one of a consortium of healthy microbes, antibiotic drugs and pre/ pro-/ syn-/ post-biotic compounds or fecal microbiome transplant which could modulate the disease microbiome composition towards a healthy equilibrium.
It is respectfully submitted that this is not persuasive because the instant case is not analogous to the argued Classen case law. Here, the recited administering step does not happen in all embodiments of the claimed invention, while in Classen, the " physical step of immunization on the determined schedule … after selection of a lower risk schedule" did occur in all embodiments of the claimed invention. In Classen was the administering step was according to the lower risk schedule. In the instant claims, not all of the treatments can actually treat what falls into each risk category. Without that affirmative recitation linking specific treatments to specific risks, the particular treatment consideration does not apply (see MPEP 2106.04(d)(2)).
Applicant asserts starting in pg. 22 para. 3
Referring to Example 39 "Method for Training a Neural Network for Facial Detection" in Subject Matter Eligibility Examples: Abstract Ideas, Applicant referred to pages 8-9 stating that under Step-2A analysis it is explicitly mentioned that "The claim does not recite any of the judicial exceptions enumerated in the 2019 PEG. For instance, the claim does not recite any mathematical relationships, formulas, or calculations. While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims. Further, the claim does not recite a mental process because the steps are not practically performed in the human mind. Finally, the claim does not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people. Thus, the claim is eligible because it does not recite a judicial exception". In line with Example 39, the claimed subject matter creates a database of sensory protein sequences of all the organisms, wherein the database of sensory protein sequences comprises information pertaining to the proteins of all fully sequenced bacteria obtained from a plurality of public repositories 124. …. Applicant asserts that, though the claim limitations are based on mathematical concepts, the mathematical concepts are not recited in the claims. Further, Applicant asserts that the claimed subject matter does not recite a mental process because the steps are not practically performed in the human mind rather performed by the hardware processors. The claimed steps cannot be viewed individually, the claimed steps as a whole are providing a technical solution of efficiently creating a database of sensory protein sequences of a plurality of organisms and generating a classification model to reduce the risk of colorectal cancer.
It is respectfully submitted that this is not persuasive because the argued Example 39 and the instant claims are not analogous. Example 39 considers a hypothetical method for training a neural network for facial detection. The exemplified claim comprises modifying a set of digital facial images by performing digital transformation steps (e.g., smoothing and contrast reduction) and training a neural network in two stages using unmodified facial images, modified facial images, and non-facial images. The Examiner agrees with the statement provided in the Guidance that “the claim does not recite a mental process because the steps are not practically performed in the human mind” (pg. 9 of ‘Subject Matter Eligibility Examples: Abstract Ideas’). However, the Examiner considers the exemplified process as non-analogous to that recited by the instant claims because, here the identified mathematical concepts are indeed recited in the claims (i.e. computing/calculating). Further support for the mathematical techniques used in the claims is provided in the specification at [030] which discloses that computation of sensory protein abundance can be performed by calculation of parameters. The mental processes were identified as described in the Claim Rejections above because a human mind is sufficiently capable of evaluate risk based on data identified, select data according to a criteria, gather identified data to create a database; make a random data selection from a list of available data; and evaluate the importance of data. Under BRI, the recited “extracting a data from the plurality of public repositories," "identifying all annotated sensory proteins from the extracted data," and "performing a sequence alignment" constitutes identifying data from a public database looking for keyword searches and comparing two sequences. Under BRI, the recited “generating, via the one or more hardware processors, sensory protein abundance profiles of a set of control versus adenoma samples, a set of control versus carcinoma samples, and a set of adenoma versus carcinoma samples obtained from publicly available data” constitutes organization/collation of data; in which a human mind could write down the number of sequences present in a set of a samples with pen and paper. The Examiner contends that the conclusion of eligibility in Example 39 arises from the particular features of the claimed process, and should not be generalized as an axiom regarding eligibility of claims that recite training of neural networks.
Applicant asserts starting in pg. 23 para. 4
Further, Referring 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 was not directed to "the abstract idea of using a 'mathematical equation that permits rotating multidimensional data"' even though they may have involved mathematical concepts. Similar to the above referenced Federal Circuit decision, Applicant's claimed 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 colorectal cancer in a person is not directed to "the abstract idea of using a mathematical equation" even though they may have involved mathematical concepts
It is respectfully submitted that this is not persuasive because the argues case law and the instant claims are not analogous. There is not recitation in the claims regarding "real-time" execution of steps. Indeed, as it was described above the step to classify and sort particles into at least two populations remains identified as a mental step because, under BRI, the human mind is capable to classify (i.e. evaluate data) and sort particles.
Applicant asserts starting in pg. 24 para. 9
Applicant's claimed administration module 122 is configured to provide/administer a therapeutic construct to the person depending on the risk of the colorectal cancer. The administration module 122 uses at least one of a consortium/ construct of healthy microbes, antibiotic drugs and pre-/ pro-/ syn-/ post-biotics or fecal microbiome transplant that would help the patient's gut microbiome to attain a healthy equilibrium without any adverse health effects. The therapy may be provided in the form of anyone (or a combination) of the known routes of administrations like intravenous solution, sprays, patches, band-aids, pills or syrup. The therapeutics is suggested as a consortium of microbes based on their (inverse) correlation with the disease microbiome which can contribute to the therapeutic treatment for prediabetes by modulating the disease microbiome towards healthy equilibrium … Applicant's claimed subject matter recites the technical advancement in terms of early assessment of colorectal cancer by administering the construct to the person depending on the risk of the colorectal cancer
It is respectfully submitted that this is not persuasive because there is no recitation of an administration module. However, the argued "administering step" consist of administrating a therapeutic construct to the person depending on the risk of the colorectal cancer which is not sufficient to integrate that abstract idea into a practical application. There is no evidence linking specific treatments to specific risks. In the instant claims, not all of the treatments can actually treat what falls into each risk category. Without that affirmative recitation linking specific treatments to specific risks, the particular treatment consideration does not apply (see MPEP 2106.04(d)(2)).
Applicant asserts starting in pg. 30 para. 4
Referring to Illumina, Inc. v. Ariosa Diagnostics, Inc.,Roche Sequencing Solutions, Inc., and Roche Molecular Systems, Inc, (Fed. Cir. 2020), the Federal Circuit concludes that "the disclosed claims of the invention are not directed to the natural phenomenon but rather to a patent-eligible method that utilizes it. The claimed methods utilize the natural phenomenon that are discovered by employing physical process steps and human-engineered size parameters to selectively remove larger fragments of cell-free DNA and thus enrich a mixture in cell-free fetal DNA. Claims are directed to methods for preparing a fraction of cell-free DNA that is enriched in fetal DNA. The methods include specific process steps-size discriminating and selectively removing DNA fragments that are above a specified size threshold-to increase the relative amount of fetal DNA as compared to maternal DNA in the sample. Moreover, the claimed methods achieve more than simply observing that fetal DNA is shorter than maternal DNA or detecting the presence of that phenomenon. The claims include physical process steps that change the composition of the mixture, resulting in a DNA fraction that is different from the naturally occurring fraction in the mother's blood. methods of preparing a DNA fraction." In line with the judgement from the Federal Circuit for the above case, Applicant's claimed subject matter discloses generating sensory protein abundance profiles of case-control samples obtained from publicly available data, applying a random forest classifier on the generated sensory protein abundance profiles of case-control samples to generate a classification model, quantifying the abundance of a sensory protein from the sequenced metagenomic reads using the database of sensory protein sequences, assessing the risk of the person to be in risk of colorectal cancer using the classification model and the quantified abundance of the sensory protein in the metagenomic sample of the person by including physical process step of administering a therapeutic construct to the person depending on the risk of the colorectal cancer.
It is respectfully submitted that this is not persuasive because the argues case law and the instant claims are not analogous. The instant claims recite naturally occurring DNA fragments, which is considered to be a law of nature or natural phenomena (a natural product), and does not simply claim a patent-eligible method that utilizes it. The fact that the claimed method selectively removes specified size threshold-to increase the relative amount of fetal DNA as compared to maternal DNA in the sample, does not negate that the remaining DNA fragments were not already in that sample originally; therefore representing a natural product.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claim 1 is rejected under 35 U.S.C. 103(a) as being unpatentable over Dahmus (“The gut microbiome and colorectal cancer: a review of bacterial pathogenesis” Journal of gastrointestinal oncology 9(4):769 (2018)) in view of Maltsev (“Sentra, a database of signal transduction proteins” Nucleic Acids Res. 30(1):349-350 (2002)) as evidenced by Punta (“The Pfam protein families database” Nucleic Acids Res. 40:D290-D301 (2012)) in view of Armour (“A Metagenomic Meta-analysis Reveals Functional Signatures of Health and Disease in the Human Gut Microbiome” mSystems 4(4) (2019)) in view of Dai (“Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers” Microbiome 6:70 (2018)) in view of Chen (“Fecal microbiota transplantation in cancer management: Current status and perspectives” Int. J. Cancer: 145:2021–2031 (2019)), as cited on the 10/21/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 1 recites:
method for assessing the risk of colorectal cancer (CRC) in a person, the method comprising … assessing, via the one or more hardware processors, the risk of the person to be in the CRC diseased state using the respective classification models and the computed abundance of the sensory protein in the metagenomic sample of the person, wherein the assessment results in the categorization of the person either in a low risk, a medium risk or a high risk of colorectal cancer diseased state based on a predefined criteria
• Dahmus teaches multiple pathogenic microorganisms in the gut microbiome focusing on bacterial pathogenesis, evidence of association with colorectal cancer and the proposed mechanisms of carcinogenesis (pg. 769 para. 1); wherein a high fiber diet was associated with a decreased risk of F. nucleatum-positive CRCs (i.e. low risk) (pg. 772 col. 2 para. 4); wherein a study comparing bacteremia with S. gallolyticus compared to all other S. bovis subtypes, observed an incidence of CRC of 71% for S. gallolyticus versus 17% for all other subtypes (i.e. reading on a range 0-100% risk – low to high – with the predefined criteria being the presence of certain microbiome subtypes over others) (pg. 770 col. 2 para. 4).
• Dahmus does not teach "abundance of sensory proteins." However, Maltsev teaches a database created to identify signal transduction proteins (i.e. abundance of sensory proteins) (pg. 349 col. 1 para. 1).
• Dahmus does not teach "using the respective classification models and the computed abundance of the sensory protein in the metagenomic sample of the person". However Armour teaches meta-analysis of microbiome gene functions (i.e. reading on proteins) that spans multiple disease types (pg. 2 para. 5) done on publicly available fecal metagenomic samples (pg. 1 para. 1); wherein data included colorectal cancer data (pg. 5 Fig. 2) to investigate how gut microbiome protein family richness, composition, and dispersion relate to disease (pg. 3 para. 2) and identify abundance of KEGG modules linked to disease using MetaPhlAn2 (i.e. bioinformatics software reading on one or more hardware processors) which included characterization of chemosensory proteins (i.e. sensory proteins) (pg. 13 para. 4 and Table S6 lines 2957 – 2961); wherein functional composition analysis of the microbiome used random forest classifiers to characterize carcinoma and adenoma samples along with comparison between both along with the respective classification sensitivity and specificity model (pg. 9 Fig. 4).
creating, via one or more hardware processors, a database of sensory protein sequences of a plurality of organisms, wherein the database of sensory protein sequences comprises information pertaining to the sensory proteins of all fully or partially sequenced bacterial genomes obtained from a plurality of public repositories, wherein the creating further comprises:
extracting data from the plurality of public repositories,
identifying all annotated sensory proteins from the extracted data using a set of keyword searches,
performing a sequence alignment to identify a set of less characterized sensory protein sequences
• Dahmus does not teach the recitation above. However, Maltsev teaches a database created to identify signal transduction proteins (i.e. abundance of sensory proteins) from 43 fully sequenced prokaryotic genomes and sequences (i.e. fully sequenced bacterial genomes) from SWISS-PROT and TrEMBL public databases (i.e. extracting a data from the plurality of public repositories) (ptg. 349 col. 1 para. 1); wherein SWISS-PROT and TrEMBL were analyzed using the Pfam database which allows for external searches as evidenced by Punta (pg. D291 col. 1 para. 2 Punta) building of a high-quality multiple sequence alignment as evidence by Punta (pg. D290 col. 2 para. 3 Punta) to identify a large series of 3526 families noted as domains of unknown function ( i.e. identify a set of less characterized sensory protein sequences) as evidence by Punta (pg. D291 col. 2 para. 6 Punta).
filtering the results of the sequence alignment based on 95% identity, 95% coverage and an e-value of cut-off 1.0*e-5 (0.00001) to identify a set of additional sensory protein sequences
• Dahmus does not teach the recitation above. However, Maltsev teaches a range of sequence coverage and residue coverage for a number of complete proteomes as evidence by Punta (pg. D297 Table 2 Punta); and the use of low E-values to avoid inclusion of sequences from other families (overlaps) as evidence by Punta (pg. D298 col. 1 para. 2 Punta); wherein data available in Pfam includes information on conservation of family signature residues (i.e. identity) as evidence by Punta (pg. D291 col. 1para. 3 Punta) and gathering thresholds chosen with the goal of maximizing coverage while excluding any false positive matches as evidence by Punta (pg. D297 col. 1 para. 2 Punta).
collating the sensory protein sequences and the sequences identified through sequence alignment to create the database of sensory protein sequences
• Dahmus does not teach the recitation above. However, Maltsev teaches that analyses results from the extracted data from public databases were parsed and stored in a relational database from which a subset of signal transduction proteins containing conserved domains relevant to signal transduction (i.e. sensory proteins) was extracted (pg. 349 col. 2 para. 2) (i.e. collating the sensory protein sequences and the sequences identified through sequence alignment to create the database of sensory protein sequences); wherein BLAST was used for large homolog searches against the non-redundant database (pg. 349 col. 2 para. 3).
generating, via the one or more hardware processors, sensory protein abundance profiles of a set of control versus adenoma samples, a set of control versus carcinoma samples, and a set of adenoma versus carcinoma samples obtained from publicly available data;
applying, via the one or more hardware processors, a random forest classifier on the generated sensory protein abundance profiles of the set of control versus adenoma samples, the set of control versus carcinoma samples, and the set of adenoma versus carcinoma samples to generate their respective classification models
quantifying, via the one or more hardware processors, the abundance of a sensory protein from the sequenced metagenomic reads using the database of sensory protein sequences
• Dahmus does not teach the recitation above. However Armour teaches meta-analysis of microbiome gene functions (i.e. reading on proteins) that spans multiple disease types (pg. 2 para. 5) done on publicly available fecal metagenomic samples (pg. 1 para. 1); wherein data included colorectal cancer data (pg. 5 Fig. 2) to investigate how gut microbiome protein family richness, composition, and dispersion relate to disease (pg. 3 para. 2) and identify abundance of KEGG modules linked to disease using MetaPhlAn2 (i.e. bioinformatics software reading on one or more hardware processors) which included characterization of chemosensory proteins (i.e. sensory protein abundance profiles) (pg. 13 para. 4 and Table S6 lines 2957 – 2961); wherein functional composition analysis of the microbiome used random forest classifiers to characterize carcinoma and adenoma samples along with comparison between both along with the respective classification sensitivity and specificity model (pg. 9 Fig. 4).
collecting a microbiome sample from a body site of the person for the assessment of the risk of CRC, wherein the microbiome sample comprising microbial cells; extracting DNA from the microbial cells; sequencing, via a sequencer, using the extracted DNA to get sequenced metagenomic reads
• Dahmus does not teach the recitation above. However, Dai teaches the use of extracted raw DNA sequences from colorectal cancer fecal samples for metagenomics analysis (pg. 8 col. 2 para. 4) wherein whole-genome shotgun sequencing of the samples was carried out followed by the removal of human sequences and classification of the unmapped microbial reads (pg. 9 col. 1para. 1); wherein the gut microbiota is studied as a risk factor for colorectal cancer (pg. 1 col. 1 para. 1); wherein proteins involved in phosphonate and phosphinate metabolism were mapped (i.e. pathways that comprise sensory proteins) (pg. 7 col. 1 para. 1).
administrating a therapeutic construct to the person depending on the risk of the colorectal cancer, wherein the therapeutic construct comprises one or more non- pathogenic Healthy Therapeutic Markers (HTMs), a plurality of antibiotic drugs targeted against Disease Markers, prebiotics, probiotics, syn biotics, post biotics or fecal microbiome transplant to help the person's gut microbiome to attain a healthy equilibrium, wherein the plurality of Healthy Therapeutic Markers (HTMs) comprises one or more of Candidatus saccharibacteria, Fibrobacter succinogenes, Haliangium ochraceum, Calothrix sp., Lactobacillus sanfranciscensis, Methanocaldococcus infernus, Nostoc punctiforme, Planctomyces limnophilus, Sphingobium chlorophenolicum, Stigmatella aurantiaca, or Veillonella parvula, and administered either alone or in concoction for therapeutic purposes, wherein the Disease Marker (DM) comprises Solitalea canadensis, wherein the therapeutic construct comprises one or more of: 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 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 natural or synthetically derived compounds which target Disease Markers (DMs), wherein the natural or synthetically derived compounds are non-toxic and do not cause any adverse effects
• Dahmus does not teach the recitation above. However, Chen teaches fecal microbiota transplantation in cancer management for reconstruction of intestinal microbiota, amelioration of bile acid metabolism, and modulation of immunotherapy efficacy (i.e. reading on administrating a therapeutic construct to the person depending on the risk of the cancer) (pg. 2021 para. 1); wherein the use of probiotics as a novel and effective approach to for reconstruction of intestinal microbiota (i.e. reading on Healthy Therapeutic Markers) involve Bifidobacteria, lactobacilli (i.e. reading on any Lactobacillus species in the Lactobacilli genus – hence one or more of: species and strains belonging to same genus, wherein the species and strains are non-pathogenic), and Streptococcus thermophilus (pg. 2026 col. 1 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claim 1, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dhamus in view of Maltsev, Armour, Dai and Chen because all references disclose methods for microbiome investigation. The motivation would have been to:
• characterize proteins related to the complex signaling and regulatory networks that govern transcriptional responses to various environmental and developmental conditions (pg. 349 col. 1 para. 2 Maltsev);
• reveal features of the microbiome that may be useful for the development of microbiome-based diagnostics (pg. 1 para. 1 Armour);
• evaluate alterations in gut microbiota in different populations and several bacterial species found to contribute to the tumorigenesis in colorectal cancer (pg. 1 para. 1 Dai) and
• manipulate the gut microbial populations with therapeutic intent (pg. 2021 para. 1 Chen).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the microbiome investigation method of Dhamus to the methods by Maltsev, Armour, Dai and Chen because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for microbiome investigation.
Regarding “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”; MPEP 2144.05 “Obviousness of Similar and Overlapping Ranges, Amounts, and Proportions (E9_R-01.2024)” affirms that one of ordinary skill in the art would be motivated to optimize a parameter when there is evidence in the record that the prior art recognized that. Thus the teachings by Maltsev would motivate one of ordinary skill in the art to modify the filtering values to obtain high identity, high coverage and low e-value to improve the quality of the data being filtered for the database created and to yield better sequences matches (with less sequences overlaps when it comes to e-values) being identified by the method.
B. Claim 6 is rejected under 35 U.S.C. 103(a) as being unpatentable over Dahmus, Maltsev, Armour, Dai and Chen as applied to claim 1 above further in view of Li (“In Silico Estimation of Chemical Carcinogenicity with Binary and Ternary Classification Methods” Mol. Inf. 34:228– 235 (2015)), as cited on the 10/21/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 6 recites:
wherein the step of assessing the risk is based on a maximum score from a ternary classification, wherein the ternary classification is derived using outputs of the respective binary classification models based on a predefined condition
• Dahmus does not explicitly teach the recited limitation above. However, Li teaches the ternary classification models and binary classification models for chemical carcinogenicity risk assessment (pg. 232 col. 2 para. 4); wherein binary and ternary classifications were build using the same data (pg. 229 col.1 para. 3) with random forest constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees (i.e. 230 col. 1 para. 2); wherein the development of binary classification models was followed by the generation of ternary classification models (pg. 228 col. 2 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claim 6, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dhamus, Maltsev, Armour, Dai and Chen in view of Li because all references disclose methods for assessing cancer risk. The motivation would have been to correctly distinguish the categories of compounds and provide helpful information for risk assessment (pg. 229 col. 1para. 1 Li). Therefore it would have been obvious to one of ordinary skill in the art to substitute the assessing cancer risk method of Dhamus, Maltsev, Armour, Dai and Chen to the methods by Li because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for assessing cancer risk.
C. Claim 7 is rejected under 35 U.S.C. 103(a) as being unpatentable over Dahmus, Maltsev, Armour, Dai and Chen as applied to claim 1 above further in view of Dai as cited on the 10/21/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 7 recites:
wherein the sample is collected in the form of one or more of saliva, stool, blood, body fluids, or swabs from at least one body site of the person, wherein the body site comprising one or more of gut, oral, or skin of the person
• Dahmus does not explicitly teach the recited limitation above. However, Dai teaches the use of extracted raw DNA sequences from colorectal cancer fecal samples for metagenomics analysis (pg. 8 col. 2 para. 4) wherein whole-genome shotgun sequencing of the samples was carried out followed by the removal of human sequences and classification of the unmapped microbial reads (pg. 9 col. 1para. 1); wherein the gut microbiota is studied as a risk factor for colorectal cancer (pg. 1 col. 1 para. 1).
One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for metagenomics analysis.
Rationale for combining (MPEP §2142-2143)
Regarding claim 7, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dhamus, Maltsev, Armour, Dai and Chen in view of Dai because all references disclose methods for metagenomics analysis. The motivation would have been to evaluate alterations in gut microbiota in different populations and several bacterial species found to contribute to the tumorigenesis in colorectal cancer (pg. 1 para. 1 Dai). Therefore it would have been obvious to one of ordinary skill in the art to substitute the metagenomics analysis method of Dhamus, Maltsev, Armour, Dai and Chen to the methods by Dai because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for metagenomics analysis.
D. Claims 9-10 are rejected under 35 U.S.C. 103(a) as being unpatentable over Dahmus, Maltsev, Armour, Dai and Chen as applied to claim 1 above further in view of Maltsev as cited on the 10/21/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 9 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 the use of clustering algorithms comprising BLASTCLUST, CLUSTALW, VSEARCH and heuristic techniques of identifying sequence similarity
Claim 10 recites:
wherein the plurality of public repositories comprises one or more of NCBI database, Protein Data Bank, KEGG database, PFAM database or EggNOG
• Dahmus does not explicitly teach the recited limitation above. However, Maltsev teaches the use of BLAST for large homolog searches against the non-redundant database (i.e. Basic Local Alignment Search Tool (BLAST) as in claim 9) (pg. 349 col. 2 para. 3); wherein public databases SWISS-PROT and TrEMBL were analyzed using the public Pfam database (i.e. PFAM database as in claim 10) which allows for external searches as evidenced by Punta (pg. D291 col. 1 para. 2 Punta).
One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for metagenomics analysis.
Rationale for combining (MPEP §2142-2143)
Regarding claims 9-10, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dhamus, Maltsev, Armour, Dai and Chen in view of Maltsev because all references disclose methods for metagenomics analysis. The motivation would have been to characterize proteins related to the complex signaling and regulatory networks that govern transcriptional responses to various environmental and developmental conditions (pg. 349 col. 1 para. 2 Maltsev). Therefore it would have been obvious to one of ordinary skill in the art to substitute the metagenomics analysis method of Dhamus, Maltsev, Armour, Dai and Chen to the methods by Maltsev because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for metagenomics analysis.
E. Claim 11 is rejected under 35 U.S.C. 103(a) as being unpatentable over Dahmus, Maltsev, Armour, Dai and Chen as applied to claim 1 above further in view of Armour in view of Swan (“Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology” OMICS 17(12):595-610 (2013)) in view of Nembrini (“The revival of the Gini importance?” Bioinformatics, 34(21):3711–3718 (2018)) in view of Menze (“A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data” BMC Bioinformatics 10:213 (2009)) in view of Fatima (“Incremental Wrapper Based Random Forest Gene Subset Selection for Tumor Discernment” DEXA 2018 International Workshops, BDMICS, BIOKDD, and TIR, Regensburg, Germany, September 3–6 Proceedings (2018)) in view of Wei (“The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics” PLoS One 8(7):e67863 (2013)), as cited on the 10/21/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 11 recites:
wherein the step of generating classification models comprises: applying a Random Forest (RF) approach on the sensory protein abundance profiles of sequenced metagenomic reads;
• Dahmus does not explicitly teach the recited limitation above. However, Armour teaches meta-analysis of microbiome gene functions (i.e. reading on proteins) done on publicly available fecal metagenomic samples (pg. 1 para. 1) to identify abundance of KEGG modules linked to disease using MetaPhlAn2 (i.e. bioinformatics software reading on one or more hardware processors) which included characterization of chemosensory proteins (i.e. sensory proteins) (pg. 13 para. 4 and Table S6 lines 2957 – 2961); wherein functional composition analysis of the microbiome used random forest classifiers (pg. 9 Fig. 4).
selecting a random set of sequenced metagenomic reads comprising 90% of fecal/ stool microbiome samples as a training set and comprising remaining 10% as a test set.
• Dahmus does not explicitly teach the recited limitation above. However, Swan teaches the Random Forest classification method (pg. 600 col. 2 para. 3) producing the model on a training set and testing it on an independent test set (pg. 601 col. 2 para. 4) using 10-fold cross validation in a database split up ten times, producing ten training sets each consisting of 90% of the data and ten test sets containing the remaining 10% (pg. 602 col. 1 para. 1).
performing 10 replicates on 10-fold cross-validation on the training set to build 100 cross-validation RF models;
capturing an importance of each of the features included in cross-validation models in terms of GINI index;
selecting a predefined number of 'important' features based on GINI index values from each of the 100 cross-validation RF models to obtain a feature subset; wherein value of the predefined number vary from 2 to 'N', wherein 'N' is total number of features, wherein each feature in the sub-set of features, obtained by choosing the predefined number of 'important' features from each of the 100 cross-validation RF models
• Dahmus does not explicitly teach the recited limitation above. However, Nembrini teaches the use of GINI indexing as the splitting criterion in classification trees to identify variable importance measures for random forest models(pg. 3711 col. 1 para. 1); wherein the most important parameters includes the size of the random subsets of variables (i.e. obtain a feature subset; wherein value of the predefined number vary from 2 to 'N', wherein 'N' is total number of features, wherein each feature in the sub-set of features) considered for splitting (the so-called mtry value) with the default value for mtry is
√
p
for classification and survival forests (i.e. selecting a predefined number for most important parameters) (pg. 3712 col. 2 para. 2); wherein real data applications for variable importance measures included comparing results obtained from a 10-fold cross validation, repeated 10 times (i.e. performing 10 replicates on 10-fold cross-validation on the training set to build 100 cross-validation RF models and obtained by choosing the predefined number of 'important' features from each of the 100 cross-validation RF models); wherein to select relevant variables, a usual approach is to select a fixed number or percentage of the highest ranking variables (pg. 3713 col. 2 para. 5).
ranking each of the features in the feature sub-set, on the basis of the sum of their GINI index values;
• Dahmus does not explicitly teach the recited limitation above. However, Menze teaches a method for feature selection based on a recursive feature elimination using the Gini importance of random forests (pg. 1 para. 2); wherein the decrease in Gini impurity resulting from this optimal split is recorded and accumulated for all nodes in all trees in the random forest model (pg. 3 col. 1 para. 1) to give an overall score and relative ranking of the features (pg. 2 col. 2 para. 3).
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;
• Dahmus does not explicitly teach the recited limitation above. However, Fatima teaches the incremental wrapper based feature subset selection method (pg. 163 para. 6)to evaluate the quality of the feature subset (i.e. feature evaluation model) (pg. 162 para. 4); wherein features are ranked in the list in decreasing order as, feature with the highest score is first in the list and so on (i.e. cumulatively adding the next ranked feature in a sub-set of features with the highest scoring feature on top) (pg. 163 para. 6); wherein the algorithm selects the feature with the highest score and make it the best feature subset after computing the accuracy with the selected learner. It computes the accuracy of the next feature subset which is developed by inducing the next highest scoring feature in the existing subset (i.e. including features of the previous 'evaluation' model) wherein the current subset is selected as the best feature subset if its accuracy is greater than the previously computed accuracy (pg. 163 para. 7).
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;
• Dahmus does not explicitly teach the recited limitation above. However, Wei teaches measures for evaluation of the performance of classification algorithms which involves balanced accuracy measures (i.e. reading on balancing score), Matthews Correlation Coefficient and Area under the curve (AUC) measures (pg. 4 col. 2 para. 2).
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 a training model and the confidence probability of the prediction to be 'case'(control versus adenoma: case adenoma; control versus carcinoma: case carcinoma; adenoma versus carcinoma: case carcinoma) were accounted
• Dahmus does not explicitly teach the recited limitation above. However, Swan teaches the application validation of classification methods on test samples for identification of biomarkers in cancer cases versus control (pg. 604 Table 3); wherein various metrics are reported alongside proteins to show the probability that the correct identification has been made (i.e. confidence probability) (pg. 598 col. 2 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claim 11, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dhamus, Maltsev, Armour, Dai and Chen in view of Armour, Swan, Nembrini, Menze, Fatima and Wei because all references disclose methods for metagenomics analysis. The motivation would have been to:
• reveal features of the microbiome that may be useful for the development of microbiome-based diagnostics (pg. 1 para. 1 Armour);
• identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups (pg. 595 para. 1 Swan);
• incorporate a unified framework to compute a fast variable importance measures for random forest models (pg. 3717 col. 1 para. 4 Nembrini);
• use feature selection based on Gini importance to identify this optimal subset of features (pg. 1 para. 3 Menze);
• significantly improve performance by working on a selective discriminative subset of prognostic genes as compare to the raw data (pg. 161 para. 1 Fatima);
• evaluate whether the training and testing sets are balanced or not (pg. 8 col. 2 para. 3 Wei) and
• differentiate the nature of the training and testing data sets (pg. 8 col. 2 para. 2 Wei).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the metagenomics analysis method of Dhamus, Maltsev, Armour, Dai and Chen to the methods by Armour, Swan, Nembrini, Menze, Fatima and Wei because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for metagenomics analysis.
F. Claim 12 is rejected under 35 U.S.C. 103(a) as being unpatentable over Dahmus, Maltsev, Armour, Dai and Chen as applied to claim 1 above further in view of Maltsev further in view of Steinegger (“Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold” Nat. Methods 16:603–606 (2019)) in view of Angly (“The GAAS Metagenomic Tool and Its Estimations of Viral and Microbial Average Genome Size in Four Major Biomes” PLOS Comput. Biol. 5(12): e1000593 (2009)), as cited on the 10/21/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 12 recites:
further comprising 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,
• Dahmus does not explicitly teach the recited limitation above. However, Maltsev teaches a database created to identify signal transduction proteins (i.e. sensory proteins) from 43 fully sequenced prokaryotic genomes and sequences (i.e. fully sequenced bacterial genomes) from SWISS-PROT and TrEMBL public databases (i.e. extracting a data from the plurality of public repositories) (ptg. 349 col. 1 para. 1); wherein SWISS-PROT and TrEMBL were analyzed using the Pfam database which allows for external searches as evidenced by Punta (pg. D291 col. 1 para. 2 Punta) building of a high-quality multiple sequence alignment as evidence by Punta (pg. D290 col. 2 para. 3 Punta) to identify a large series of 3526 families noted as domains of unknown function ( i.e. identify a set of poorly annotated or characterized sensory protein sequences) as evidence by Punta (pg. D291 col. 2 para. 6 Punta).
wherein the hits satisfying a minimum e-value threshold of 1.0*e-5 (0.00001) are considered as correct matches;
• Dahmus does not explicitly teach the recited limitation above. However, Maltsev teaches a range of sequence coverage and residue coverage for a number of complete proteomes as evidence by Punta (pg. D297 Table 2 Punta); and the use of low E-values to avoid inclusion of sequences from other families (overlaps) as evidence by Punta (pg. D298 col. 1 para. 2 Punta).
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;
• Dahmus does not explicitly teach the recited limitation above. However, Steinegger teaches a method for recovering sequence reads from complex metagenomes (pg. 603 col. 1 para. 1); reporting cumulative values for total percentages of reads (i.e. cumulative matches of the sequenced metagenomic reads to form a count) extracted from samples (i.e. sample from which the sequenced metagenomic reads were obtained) matched to different databases (pg. 605 col. 1 para. 1); wherein sequences with repeat coding regions were detected (i.e. reading on identification of coding regions) (pg. 607 col. 2 para. 4) while using a tool for simulating strain diversity (pg. 607 col. 2 para. 6).
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,
• Dahmus does not explicitly teach the recited limitation above. However, Angly teaches the estimation of average genome length (i.e. reading on cumulative length of the nucleotide bases to form a covered base length) for over 150 viral and microbial metagenomes (pg. 1 Abstract).
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 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
• Dahmus does not explicitly teach the recited limitation above. However, Angly teaches metagenomic studies of composition and diversity of uncultured viral and microbial communities (pg. 1 para. 1); wherein genome relative abundance of sequence reads (i.e. measurement comprising ratio) (pg. 1 para. 1) was calculated over total number of sequences in the database (pg. 8 col. 2 para. 2) reporting calculation in Megabases (pg. 5 col. 1 para. 1); wherein microbial strains were identified as having largely identical genomes with a fraction of coding for additional genes accounting for the differences in genome length (i.e. 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) (pg. 8 col. 2 para. 3).
by Maltsev, Steinegger and Angly to the method by Dahmus to characterize proteins related to the complex signaling and regulatory networks that govern transcriptional responses to various environmental and developmental conditions (pg. 349 col. 1 para. 2 Maltsev); to recover several times more protein sequences from complex metagenomes (pg. 603 col. 2 para. 2 Steinegger) and to determine what viruses or microorganisms exist in natural communities and what metabolic activities they encode (i.e. comprising protein functions) (pg. 2 col. 1 para. 1 Angly). One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for metagenomics analysis.
Rationale for combining (MPEP §2142-2143)
Regarding claim 12, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dhamus, Maltsev, Armour, Dai and Chen in view of Maltsev, Steinegger and Angly because all references disclose methods metagenomics analysis. The motivation would have been to:
• to characterize proteins related to the complex signaling and regulatory networks that govern transcriptional responses to various environmental and developmental conditions (pg. 349 col. 1 para. 2 Maltsev);
• recover several times more protein sequences from complex metagenomes (pg. 603 col. 2 para. 2 Steinegger) and
• determine what viruses or microorganisms exist in natural communities and what metabolic activities they encode (i.e. comprising protein functions) (pg. 2 col. 1 para. 1 Angly).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the metagenomics analysis method of Dhamus, Maltsev, Armour, Dai and Chen to the methods by Maltsev, Steinegger and Angly because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for metagenomics analysis.
Regarding “wherein the hits satisfying a minimum e-value threshold of 1.0*e-5 (0.00001) are considered as correct matches”; MPEP 2144.05 “Obviousness of Similar and Overlapping Ranges, Amounts, and Proportions (E9_R-01.2024)” affirms that one of ordinary skill in the art would be motivated to optimize a parameter when there is evidence in the record that the prior art recognized that. Thus the teachings by Maltsev would motivate one of ordinary skill in the art to modify the filtering values to obtain high identity, high coverage and low e-value to improve the quality of the data being filtered for the database created and to yield better sequences matches (with less sequences overlaps when it comes to e-values) being identified by the method.
G. Claim 15 is rejected under 35 U.S.C. 103(a) as being unpatentable over Dahmus, Maltsev, Armour, Dai and Chen as applied to claim 1 above further in view of Choi ("Proteomic and cytokine plasma biomarkers for predicting progression from colorectal adenoma to carcinoma in human patients." Proteomics 13(15):2361-2374 (2013)), as cited on the 10/21/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 15 recites:
wherein the low risk indicates condition of the person healthy, the medium risk indicates the condition of the person Adenoma or Polyps, the high risk indicates the condition of the person Carcinoma or Advanced Adenoma.
• Dahmus does not explicitly teach the recited limitation above. However, Choi teaches proteomic analysis that identified 11 upregulated and 13 downregulated plasma proteins showing significantly different regulation patterns with diagnostic potential for predicting progression from adenoma to carcinoma (pg. 2361 Abstract); wherein CRC could be effectively controlled if the main premalignant lesion (adenoma) is detected and removed before invasion occurs (i.e. reading on a low risk correlation to adenoma) to avoid the transition and progression from adenoma to carcinoma (pg. 2362 col. 1 para. 2); wherein a constant event in colon carcinogenesis and is associated with the transition from adenoma to carcinoma (i.e. reading on a high risk correlation to carcinoma) (pg. 2369 col. 2 para. 3).
Rationale for combining (MPEP §2142-2143)
Regarding claim 15, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Dhamus, Maltsev, Armour, Dai and Chen in view of Choi because all references disclose methods for assessing cancer risk. The motivation would have been to identify potent biomarkers associated with CRC as well as its progression from adenoma to carcinoma by profiling of the proteome (pg. 2370 col. 1 para. 6 Choi).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the assessing cancer risk method of Dhamus, Maltsev, Armour, Dai and Chen to the methods by Choi because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for assessing cancer risk.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 103
The Remarks of 01/20/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 32 para. 2
Applicant humbly states that the prior art Dhamus, Maltsev, Punta, Armour, Dai, Chen does not teach Applicant's claimed features … Armour does not teach Applicant's claimed feature "generating, via the one or more hardware processors … applying … quantifying … It must be noted that, in Applicant's claimed invention, 'early' diagnosis of CRC based on a sub-set of proteins from the gut microbiome. More specifically, the determination of the diseased state is based on the abundance of sensory protein that are encoded by the genome of the bacterial groups which reside in the human gut. Further, the prediction is based on count of sensory proteins encoded in the bacterial genome. Armour does not specifically mention the determination of abundance of the sensory protein for diagnosing the colorectal cancer … Applicant humbly states that the cited prior art Chen does not teach Applicant's claimed feature "administrating a therapeutic construct … On the other hand, Applicant's claimed invention addresses the problem of early assessment of colorectal cancer in the person. … Different implementations to identify the suitable therapeutic candidates are as following: … Chen is silent on the above features … Therefore, Applicant humbly states that Dhamus, Maltsev, Punta, Armour, Chen, either individually or their hypothetical combination, fails to disclose one or more limitations of claim 1. It would not be obvious to one of ordinary skill of the art having the teaching of Dhamus, Maltsev, Punta, Armour, Chen before the effective filing date of the claimed invention.
It is respectfully submitted that this is not persuasive because all the argued teachings have been directly taught by the prior art, suggested or motivated as described in detail in the Claim Rejections above. Obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
Specifically, regarding the argued "using … applying… quantifying" steps not taught by Armour, Armour teaches a classification method for the characterization of chemosensory proteins (i.e. sensory proteins – which is a classification of proteins based on its function – hence comprising several proteins that share said function) (pg. 13 para. 4 and Table S6 lines 2957 – 2961) as described in detail in the previous office action and in this instant examination. Similarly to Armour all claim elements have been addressed. Under BRI, Chen's teachings regarding transplantation of fecal microbiome indeed reads on the administration of a therapeutic construct.
Regarding the argued different implementations to identify the suitable therapeutic candidates, the described does not constitute actual claim elements and therefore not required to be examined. However, regarding what is in fact recited, the prior art to Chen teaches the "one or more non- pathogenic Healthy Therapeutic Markers" because Chen teaches markers that have the function of reconstruction of intestinal microbiota (i.e. reading on Healthy Therapeutic Markers). "It is well-established that a determination of obviousness based on teachings from multiple references does not require an actual, physical substitution of elements." In re Mouttet, 686 F.3d 1322, 1332, 103 USPQ2d 1219, 1226 (Fed. Cir. 2012). Furthermore, Chen teaches fecal microbiota transplantation in cancer management for reconstruction of intestinal microbiota, amelioration of bile acid metabolism, and modulation of immunotherapy efficacy (i.e. reading on administrating a therapeutic construct to the person depending on the risk of the cancer) (pg. 2021 para. 1); wherein the use of probiotics as a novel and effective approach to for reconstruction of intestinal microbiota (i.e. reading on Healthy Therapeutic Markers) involve Bifidobacteria, lactobacilli (i.e. reading on any Lactobacillus species in the Lactobacilli genus – hence one or more of: species and strains belonging to same genus, wherein the species and strains are non-pathogenic), and Streptococcus thermophilus (pg. 2026 col. 1 para. 2).
Regarding the claimed "problem" solution regarding early assessment of colorectal cancer in the person and administration of therapy based on such assessment, it has been concluded that the prima facie case of obviousness has been established. MPEP 2141.III for "RATIONALES TO SUPPORT REJECTIONS UNDER 35 U.S.C. 103"; wherein "(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention." Furthermore, in this instant application, the amendments support existing claim rejections, in which the recited limitations are all addressed, see Claim Rejections above
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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/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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