DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 30 June 2025 has been entered.
Status of the Claims
Claim set entered 30 June 2025 has been entered into the application.
Claims 1 and 15-16 are amended.
Claims 34 are new.
Claims 14, 20-21, and 24-27 are previously cancelled.
Claims 19 and 22-23 are cancelled.
Claims 1-13, 15-18, and 28-34 are pending.
Priority
Acknowledgment is made of applicant’s claim for priority to PCT/US2020/061557 filed 20 November 2020 which claims further priority to U.S Provisional Application 62/938,827 filed 21 November 2019.
Claim Rejections - 35 USC § 101
The instant rejection is maintained for reason for record in the Office Action mailed 28 January 2025 and modified in view of the Amendments filed 30 June 2025. It is noted the amendments received 30 June 2025 necessitated new ground(s) of rejection.
The rejection of claims 19 and 22-23 under 35 U.S.C. 101 in the Office Action mailed 28 January 2025 is withdrawn in view of the amendments received 30 June 2025 because the claims were cancelled.
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 1-13, 15-18, and 28-34 are rejected under 35 U.S.C. 101 because the claims encompass mental processes and mathematical concepts without significantly more.
Claim analysis
Under broadest reasonable interpretation (BRI), the claims are drawn to analyzing microbial DNA and comparing microbial DNA datasets to generate a metric to make dietary and behavioral recommendations.
Following the flowchart of the MPEP 2106
Step I - Process, Machine, Manufacture or Composition
Claims1-13, 15-18, and 28-34 are drawn to a method, so a process.
2A Prong I
Identification of an Abstract Idea
Claim 1 recites:
Determining, based on the metagenome data and for each microbial species of a pre- defined set of microbial species, an indication of presence of the microbial species in the stool sample of the individual.
This step can be performed in the human mind by observing and comparing metagenomics data for each species to determine the presence or absence of the microbial species and is therefore an abstract idea.
Comparing an aggregate presence of microbial species in the stool sample from a first pre-defined subset of the pre-defined set of microbial species with an aggregate presence of microbial species in the stool sample from a second-pre-defined subset of the pre-defined set of microbial species.
This step can be performed in the human mind by observing and evaluating an aggregate presence of a microbial species of a first pre-defined set of microbial species with an aggregate presence of microbial species in the stool sample from the second pre-defined subset of the microbial species to compare the species between data sets and is therefore an abstract idea.
Wherein (i) an abundance of microbial species from the first pre-defined subset is statistically correlated with a non-disease specific indicator of good overall health.
This step can be performed in the human mind by observing and evaluating the abundance of microbial species of the first pre-defined subset to statistically correlate the microbial species to a non-disease specific indicator of good overall health and is therefore an abstract idea. This step encompasses mathematical/statistical concepts of statistically correlating microbial abundance to overall good health which reads on abstract ideas.
(ii) an abundance of microbial species from the second pre-defined subset is statistically correlated with a non-disease specific indicator of poor overall health or a scarcity of microbial species in the second pre-defined subset is statistically correlated with the non-disease specific indicator of good overall health.
This step can be performed in the human mind by observing and evaluating the abundance of microbial species of the second pre-defined subset to statistically correlate the microbial species to a non-disease specific indicator of poor overall health and is therefore an abstract idea. This step can be further performed in the human mind by observing and evaluating the scarcity of microbial species in the second pre-defined subset to statistically correlate the microbial species to a non-disease specific indicator of good overall health. This step encompasses mathematical/statistical concepts of statistically correlating microbial abundance to overall poor health which reads on abstract ideas. This step encompasses mathematical/statistical concepts of statistically correlating scarcity of microbial species to overall good health which reads no abstract ideas.
generating a metric indicative of the overall health of the individual based on a result of comparing the aggregate presence of microbial species in the stool sample from the first pre- defined subset with the aggregate presence of microbial species in the stool sample from the second pre-defined subset.
This step can be performed in the human mind by observing and comparing the aggregate presence of microbial species in the stool sample of the first and second pre-defined subset to generate a metric indicative of the overall health of an individual and is therefore an abstract idea. Here, the term “generating” is interpreted as an alternative term for performing arithmetic operations such as “calculating”. See MPEP 2106.04(a)(2)(I)(C). Thus, under broadest reasonable interpretation (BRI), the claimed limitation is generating quantitative data (i.e., a metric indicative of the overall health) or a metric (i.e., mathematical variable or numerical value) which encompasses performing mathematical operations to produce a value (i.e., metric indicative of overall health). As such, this step encompasses performing mathematical computations for generating a metric indicative of the overall health which reads on abstract ideas/mathematical concepts.
If the metric falls within a first range of values indicative of a first level of poor overall health, providing a first set of dietary or behavioral recommendations for the individual
This step can be performed in the human mind by observing, comparing, and evaluating if the metric falls with a first range of values indicative of a first level of poor overall health to provide a first set of dietary or behavioral recommendations for the individual and is therefore an abstract idea. This step further encompasses making decisions (i.e., judgements, observation, opinions) based on if the metric falls with a first range of values in order to provide dietary or behavioral recommendations to an individual which encompasses abstract ideas. This step encompasses using the mathematical operations of equalities and inequalities for determining if the value falls within a numerical range or threshold to provide first set of dietary or behavioral recommendations which reads on abstract ideas/mathematical concepts.
If the metric falls within a second range of values indicative of a second level of poor overall health, providing a second set of dietary or behavioral recommendations for the individual
This step can be performed in the human mind by observing, comparing, and evaluating if the metric falls with a second range of values indicative of a second level of poor overall health to provide a second set of dietary or behavioral recommendations for the individual and is therefore an abstract idea. This step further encompasses making decisions (i.e., judgements, observation, opinions) based on if the metric falls with a range of values in order to provide dietary or behavioral recommendations to an individual which encompasses abstract ideas. This step encompasses using the mathematical operations of equalities and inequalities for determining if the value falls within a numerical range or threshold to provide second set of dietary or behavioral recommendations which reads on abstract ideas/mathematical concepts.
wherein the first set of dietary or behavioral recommendations and the second set of dietary or behavioral recommendations are different from each other and are each selected from a group comprising (i) recommendations to increase consumption of healthy microbes directly via supplementary probiotics or fermented foods, (ii) recommendations to increase consumption of prebiotics or fiber-rich foods, (iii) recommendations to reduce consumption of high fat foods, high sugar foods, or artificial sweeteners, (iv) recommendations to increase exercise, sleep, or meditation, and (v) recommendations to avoid antibiotics that are not medically necessary.
This step describes first and second set of dietary or behavioral recommendations as different from each other. Additionally, this step can be performed in the human mind by following instructions for selecting dietary or behavioral recommendations from a group of dietary or behavioral recommendations encompassing five groups (i-v) and is therefore an abstract idea.
Claim 32 recites wherein the first and second pre-defined subsets of microbial species are identified by training a machine-learning model to discriminate microbial species correlated with the non-disease specific indicator of good overall health from microbial species correlated with the non-disease specific indicator of poor overall health.
This step can be performed in the human mind by following instructions to train a machine learning model to discriminate microbial species correlated with non-disease specific indicator of good/poor health and is therefore an abstract idea. This step encompasses training a machine learning model to discriminate microbial species correlated with non-disease specific indicator of good/poor health which encompasses performing mathematical/statistical operations and computations which reads on abstract ideas. Here, the machine learning model reads on organizing mathematical relationships between microbial species and indicators of good/poor health which further reads on abstract ideas. Furthermore, training a machine learning model amounts to performing a series of mathematical calculations in order to arrive at a final mathematical function that represents a mathematical relationship between the variables (i.e., to discriminate microbial species correlated with a non-disease specific indicator of good/poor health) which further reads on abstract ideas/mathematical concepts.
Claim 32 recites including creating a first training set comprising indications of presence of microbial species in stool samples from individuals classified as having good overall health.
This step can be performed in the human mind by organizing data (i.e., indications of presence of microbial species in stool samples from individuals classified as having good overall health) to create a first training dataset and is therefore an abstract idea.
Claim 32 recites creating a second training set comprising indications of presence of microbial species in stool samples from individuals classified as having poor overall health.
This step can be performed in the human mind by organizing data (i.e., indications of presence of microbial species in stool samples from individuals classified as having poor overall health) to create a second training dataset and is therefore an abstract idea.
Claim 32 recites training a machine-learning model on the first and second training sets.
This step can be performed in the human mind by following instruction to train a machine-learning model using first and second training sets and is therefore an abstract idea. This step encompasses training a machine-learning model which amounts to performing a series of mathematical calculations in order to arrive at a final mathematical function that represents a mathematical relationship between the variables (i.e., to discriminate microbial species correlated with a non-disease specific indicator of good/poor health) which reads on abstract ideas/mathematical concepts. Also, the machine learning model reads on organizing mathematical relationships between microbial species and indicators of good/poor health which further reads on abstract ideas.
Claims 1-13, 15-18, and 28-34 are further drawn to limitations that describe the abstract idea of claim 1.
Identification of Law of Nature/Natural Phenomenon
Claim 1 recites “(i) an abundance of microbial species from the first pre-defined subset is statistically correlated with a non-disease specific indicator of good overall health.”
This step recites a natural correlation of correlating microbial abundance of a certain microbial species to good health of an individual.
Claim 1 recites “(ii) an abundance of microbial species from the second pre-defined subset is statistically correlated with a non-disease specific indicator of poor overall health or a scarcity of microbial species in the second pre-defined subset is statistically correlated with the non-disease specific indicator of good overall health”.
This step recites a natural correlation of correlating microbial abundance of a certain microbial species to poor health of an individual.
2A Prong II - Consideration of Practical Application
Claim 1 does not recite any additional element which integrates the recited judicial exception into a practical application.
Here, in the instant case, the claims merely set forth a method of data analysis between microbial metagenomic data sets that is compared to generate a metric indicative of the overall health of an individual that is compared to a range of values to provide dietary and behavioral recommendations. As such, practicing the claims merely results in generating a metric indicative of the overall health of an individual that is compared to a range of values in order to provide dietary and behavioral recommendations. Such a result only produces new information for providing dietary and behavioral recommendations and does not provide for a practical application in the real-world realm of physical things and acts, i.e., the claims do not utilize the data gathered by the judicial exception to affect any type of change.
Claim 32 recites creating a first training set and creating a second training set. Practicing the claims merely recites making or creating dataset that will be analyzed by the abstract idea. New claim 34 was added to provide a further description regarding creating the first training set. Here, the creating the first training set encompasses performing species-level taxonomic profiling and filtering out samples in the training set after taxonomic profiling has more than a threshold level of unclassified taxonomies. The claim merely provides further description of the process of creating a dataset (i.e., performing species-level taxonomic profiling and filtering out samples in the training set after taxonomic profiling) which does not integrate the recited judicial exception into a practical application. Such a result only produces new information and does not provide for a practical application in the real-world realm of physical things and acts, i.e., the claims do not utilize the data gathered by the judicial exception to affect any type of change. Merely combining information to construct training data sets by taking existing information, manipulating the data using mathematical/statistical functions, and organizing this information into a new form does not set forth a practical application. See MPEP 2106.05(a)(2)(A)(iv).
Therefore, the claims do not utilize the data gathered by the additional element step of obtaining metagenomic sequencing data, compared an aggregate, generated a metric indicative of overall of an individual presence of microbial species, and the results of the abstract ideas to construct a practical application such as treating a subject, transformation of matter, or improving upon an existing technology.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses 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.
2B Analysis - Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea.
The recited additional element of data gathering of claims 1 and 5 does not amount to more than the recited judicial exception because obtaining metagenomic data is deemed a well-known and conventional extra-solution activity for gathering microbial sequence data. See MPEP 2106.05(g).
The recited additional element of data output of claims 1, 16, and 23 does not provide more than the recited judicial because providing an assessment is deemed a routine and well-known extra-solution activity for presenting and visualizing data with respect to gut health. See MPEP 2106.05(g).
The recited additional element of computer processes and equipment of claim 2 does not provide more than the recited judicial exception because using computer process and equipment is routine, well-known, and conventional.
The additional element of data gathering of claim 6 does not provide more than the recited judicial exception because using sequencing methods is deemed a routine and well-known extra-solution activity for gathering nucleic acid sequence data. See MPEP 2106.05(d)(II)(vii).
The recited additional element of data output of claims 22 does not provide more than the recited judicial exception because providing a recommendation is deemed a routine and well-known extra-solution activity for providing data output regarding gut health. See MPEP 2106.05(g).
In conclusion, and when viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments, filed 30 June 2025, have been fully considered but the rejection is maintained. However, upon further consideration, a new ground(s) of rejection is made in view of amendment received 30 June 25.
The Applicant requests withdrawal of the rejection under 35 U.S.C § 101 due to the amendments to claim 1. The Applicant points to the amendments to claim 1 for guidance. The Applicant states “Even if claim 1 recited an abstract idea (which Applicant does not concede), the additional features recited above integrate the abstract idea into a practical application and add significantly more to the idea such that the subject matter of claim 1 is not directed to a judicial exception.” The Applicant points to the MPEP 2106.04(d) for guidance. The Applicant states “By determining a range in which the metric indicative of the overall health of the individual falls and, based on the range of the metric, providing different dietary or behavioral recommendations for the individual selected from a group comprising the recommendations specifically recited in claim 1, the claim applies the alleged abstract idea in a meaningful way beyond generally linking its use to a particular technological environment, for example.” [remarks, pages 8-9].
In response, and as described in the 35 U.S.C § 101 above, the amendments to claim 1 encompass abstract ideas under Step 2A Prong I. Here, generating a metric indicative of the overall health of the individual, under (BRI), is interpreted as generating quantitative data (i.e., a metric indicative of the overall health) or a metric (i.e., mathematical variable or numerical value) which encompasses performing mathematical operations or mental processes to produce a value (i.e., metric indicative of overall health). Also, the term “generating” can be interpreted as an alternative term/limitation for performing arithmetic operations such as “calculating” or producing information with the human mind. See MPEP 2106.04(a)(2)(I)(C). As such, this step encompasses performing mathematical computations for generating a metric indicative of the overall health which reads on abstract ideas/mathematical concepts. Here, once the metric is generated, it is determined if the metric falls within a first and second range of values in order to make a decision with respect to providing dietary and behavioral recommendations. As such, observing and comparing the metric against thresholds to make a judgement with respect to if the metric falls within a first and second range is merely comparing data to make a decision regarding if the data falls within the said thresholds which reads on abstract ideas. Thus, claim 1 is drawn to abstract ideas and does not apply the alleged abstract idea in a meaningful way. Furthermore, and as noted above, claim 1 does not encompass any additional elements that integrates the recited judicial exception into a practical application. Here, the claims are drawn to a method for analyzing microbial DNA to provide sequence information and/or detect allelic variants within microbial population of an individuals’ gut microbiome to generate a metric to provide dietary and behavioral recommendations which does not provide a practical application of the judicial exception. See MPEP 2106.05(d)(II)(v).
The Applicant states “The additional features also add significantly more the alleged abstract idea at Step 2B of the eligibility analysis [remarks, page 9].
In response, claim 1 is drawn to merely gathering and analyzing information (i.e., metagenomic data from a stool sample of an individual) using conventional techniques (i.e., shotgun sequencing, high-throughput sequencing, PCR) and displaying the result(s) (i.e., a metric indicative of overall health of an individual to provide dietary and behavioral recommendations). See MPEP 2106.05(a)(II)(iii). Moreover, claim 1 does not add significantly more the alleged abstract idea because the claim utilizes well-known and conventional additional elements for analyzing microbial DNA to provide sequence information or detect allelic variants for generating a metric indicative of the overall health of an individual to provide dietary and behavioral recommendations. See MPEP 2106.05(d)(II) (ii-iii, v, vii).
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
It is noted the amendments received 30 June 2025 necessitated new ground(s) of rejection.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 4-7, 10, 12-13, 15-16, 18, 29, and 32-33 are rejected under 35 U.S.C. 103 as being unpatentable over Apte et al. (Patent Pub No.: US 2019/0080046, Pub Date: 14 March 2019) in view of Manichanh et al. (Gut, 2006-02, Vol.55 (2), p.205-211) in view of Casen et al. (Alimentary pharmacology & therapeutics, 2015-07, Vol.42 (1), p.71-83).
Claim 1 recites obtaining metagenome data that describes the metagenome for a stool sample of the individual.
Claim 1 recites determining, based on the metagenome data and for each microbial species of a pre- defined set of microbial species, an indication of presence of the microbial species in the stool sample of the individual.
Claim 1 recites comparing an aggregate presence of microbial species in the stool sample from a first pre-defined subset of the pre-defined set of microbial species with an aggregate presence of microbial species in the stool sample from a second-pre-defined subset of the pre-defined set of microbial species. Claim 1 recites wherein (i) an abundance of microbial species from the first pre-defined subset is statistically correlated with a non-disease specific indicator of good overall health. Claim 1 recites (ii) an abundance of microbial species from the second pre-defined subset is statistically correlated with a non-disease specific indicator of poor overall health or a scarcity of microbial species in the second pre-defined subset is statistically correlated with the non-disease specific indicator of good overall health.
Claim 1 recites generating a metric indicative of the overall health of the individual based on a result of comparing the aggregate presence of microbial species in the stool sample from the first pre-defined subset with the aggregate presence of microbial species in the stool sample from the second pre-defined subset.
Claim 1 recites if the metric falls within a first range of values indicative of a first level of poor overall health, providing a first set of dietary or behavioral recommendations for the individual.
Claim 1 recites if the metric falls within a second range of values indicative of a second level of poor overall health, providing a second set of dietary or behavioral recommendations for the individual.
Claim 1 recites wherein the first set of dietary or behavioral recommendations and the second set of dietary or behavioral recommendations are different from each other and are each selected from a group comprising (i) recommendations to increase consumption of healthy microbes directly via supplementary probiotics or fermented foods, (ii) recommendations to increase consumption of prebiotics or fiber-rich foods, (iii) recommendations to reduce consumption of high fat foods, high sugar foods, or artificial sweeteners, (iv) recommendations to increase exercise, sleep, or meditation, and (v) recommendations to avoid antibiotics that are not medically necessary.
Apte et al. (Apte) discloses the user’s microbiome features and the reference features are compared [Figure 9]. Apte discloses “comparing one or more reference features (e.g., abundance ranges, etc.) to one or more user microbiome features (e.g., abundances, etc.) associated with one or more characteristics (e.g., taxa, conditions, etc.) can be used in determining one or more significance index metrics, such as including characterizing the user as possessing the characteristic (e.g., a healthy microbiome, etc.) or not possessing the characteristic based on whether the user microbiome parameter values fall inside or outside the reference microbiome parameter ranges.” [Disclosure page 8 right col para. 0065]. Apte discloses user relative abundance features that can be compared to reference relative abundance features correlated with microorganism-related conditions and/or therapy responses [disclosure page 18 right col para. 0120], as in instant claim 1 comparing an aggregate presence of microbial species in the stool sample from a first pre-defined subset of the pre-defined set of microbial species with an aggregate presence of microbial species in the stool sample from a second-pre-defined subset of the pre-defined set of microbial species.
Apte recites “wherein determining the significance index metric for the user comprises determining the significance index metric based on the user microbiome composition features and a set of coefficients of correlations for the set of associations between the set of microorganism taxa and the at least one microorganism-related condition [claim 13]. Apte discloses “determining one or more significance index metrics can be based on one or more artificial intelligence approaches (e.g., machine learning models; such as significance index models applying artificial intelligence approaches; etc.), such as to calculate a sample's probability of coming from a user with a certain microorganism- related condition of interest or being healthy [page 12 right col para. 0092], as in claim 1 wherein (i) an abundance of microbial species from the first pre-defined subset is statistically correlated with a non-disease specific indicator of good overall health. Here, calculating a sample's probability of coming from a user with a certain microorganism- related condition of interest or being healthy makes obvious that an abundance of microbial species from the first pre-defined subset (i.e., set of microorganism taxa and the at least one microorganism related condition or a user microbiome and the at least one microorganism-related condition) is statistically correlated with a non-disease specific indicator of good overall health because a user with a certain microorganism-related condition of interest (i.e., poor health) or being healthy (i.e., overall good health).
Apte recites wherein determining the significance index metric comprises determining a propensity score for the user characterizing the association between the user microbiome and the at least one microorganism-related condition, based on the user abundances and effect size metrics determined based on the reference abundance ranges and the set of associations between the set of microorganism taxa and the at least one microorganism related condition [claim 16]. Apte recites determining the effect size metrics comprises determining a set of coefficients of correlations for the set of associations between the set of microorganism taxa and the at least one microorganism-related condition, based on a meta-analysis [claim 3] as in instant claim 1 (ii) an abundance of microbial species from the second pre-defined subset is statistically correlated with a non-disease specific indicator of poor overall health. Here, the correlation between the user microbiome and the at least one microorganism-related condition and between the set of microorganism taxa and the at least one microorganism-related condition, based on a meta-analysis, makes obvious an abundance of microbial species statistically correlated with an indicator of poor health because correlation of at least one microorganism-related condition teaches the user has a condition and/or poor health having the condition (i.e., good/poor overall health).
Apt discloses “the abundance and the direction of the association, the correlations are classified as "protective" when associated (e.g., positively associated) taxa are found in low or normal abundances [disclosure page 10 right col para. 0078], as in instant claim 1 a scarcity of microbial species in the second pre-defined subset is statistically correlated with the non-disease specific indicator of good overall health.
Apte discloses “system can be used for facilitating promoting (e.g., providing; recommending etc.) of one or more targeted therapies to users suffering from one or more microorganism-related conditions (e.g., skin-related conditions, etc.), such as based on one or more significance index metrics.” [disclosure page 3 left col para. 0032]. Apte discloses probiotic therapies [disclosure page 14 right col para 0104]. Apt discloses prebiotic therapies [disclosure page 14 right col para 0104]. Apt discloses “determining the microorganism-related condition classification comprises determining at least one of a caffeine consumption classification, an alcohol consumption classification, an artificial sweetener consumption classification, and a sugar consumption classification.” [disclosure page 17 right col para. 0115]. Apt discloses “dietary recommendations such as reducing sugar intake, increased vegetable intake, increased fish intake” [disclosure page 14 right col para 0104]. Apt discloses therapies such as increased exercise, sleep habits, meditation [disclosure page 14 right col para 0104]. Here, it would be obvious that the dietary and behavioral recommendation for the first and second set would be different because the sets can contain different microbial species which would respond differently to different types of dietary and/or behavioral interventions. It would be further obvious to recommend avoiding antibiotics that are not medically necessary because one of ordinary skill in the art would recognize that certain antibiotics can inflict collateral damage to healthy gut flora which could lead to the proliferation of unhealthy antibiotic resistant microbial species. Therefore, Apte makes obvious instant claim 1 wherein the first set of dietary or behavioral recommendations and the second set of dietary or behavioral recommendations are different from each other and are each selected from a group comprising (i) recommendations to increase consumption of healthy microbes directly via supplementary probiotics or fermented foods, (ii) recommendations to increase consumption of prebiotics or fiber-rich foods, (iii) recommendations to reduce consumption of high fat foods, high sugar foods, or artificial sweeteners, (iv) recommendations to increase exercise, sleep, or meditation, and (v) recommendations to avoid antibiotics that are not medically necessary.
Apte discloses a therapy facilitation system [figure 3], as in claim 2.
Apte recites further comprising facilitating therapeutic intervention for the user for the at least one microorganism-related condition based on the significance index metric [Apte, claim 24]. Apte discloses “Therapies (e.g., microorganism-related therapies, etc.) can include any one or more of: consumables e.g., probiotic therapies, prebiotic therapies “[disclosure page 14 right col para 0104], as in instant claim 33.
Apt does not teach obtaining metagenome data that describes the metagenome for a stool sample of the individual of claim 1. Apt does not teach determining, based on the metagenome data and for each microbial species of a pre- defined set of microbial species, an indication of presence of the microbial species in the stool sample of the individual of claim 1. Apt does not teach generating a metric indicative of the overall health of the individual based on a result of comparing the aggregate presence of microbial species in the stool sample from the first pre-defined subset with the aggregate presence of microbial species in the stool sample from the second pre-defined subset of claim 1. Apt does not teach if the metric falls within a first range of values indicative of a first level of poor overall health, providing a first set of dietary or behavioral recommendations for the individual of claim 1. Apt does not teach if the metric falls within a second range of values indicative of a second level of poor overall health, providing a second set of dietary or behavioral recommendations for the individual of claim 1. Apt does not teach claims 4-7, 10, 12-13, 15-16, 18, 29, and 32.
Manichanh et al. (Manichanh) teach using two metagenomic libraries for each group of subjects. Manichanh teaches 16S rRNA genes were screened by DNA hybridization and sequenced, and phylogenetic analysis was performed [page 205 right col ], as in claim 1 obtaining metagenome data that describes the metagenome for a stool sample of the individual.
Manichanh teaches “The metagenomic approach allowed us to detect a reduce complexity of the bacterial phylum Firmicutes as a signature of the fecal microbiota in patients with CD. It also indicated the presence of new bacterial species.” [abstract], as in claim 1 determining, based on the metagenome data and for each microbial species of a pre- defined set of microbial species, an indication of presence of the microbial species in the stool sample of the individual.
Manichanh teaches a comprehensive metagenomic approach to investigate the full range of intestinal microbial diversity. We used a fosmid vector to construct two libraries of genomic DNA isolated directly from fecal samples of six healthy donors and six patients with CD [abstract], as in claim 5.
With respect to claim 10, the claim is rendered obvious because Manichanh teaches a figure that shows comparisons of libraries derived from healthy subjects and patients with Crohn’s disease. Manichanh teaches a statistically significance difference between healthy subjects and Crohn’s disease [page 209 fig 4]. Here, Manichanh makes obvious claim 10 because the healthy library can be considered a first data set while the Crohn’s library can be considered a different pre-defined subset. Additionally, significant difference between healthy subjects and Crohn’s disease patients teaches that some of the bacteria are statistically related to healthy biomes.
It would have been obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Apte in view of Manichanh because Manichanh teaches metagenomic analysis of fecal samples to determine intestinal microbial diversity [abstract] by comparing healthy and Crohn’s disease libraries to find significances between specific microbial species (i.e., Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes [page 209 figure 4]. One of ordinary skill in the art would be motivated to combine Apte in view of Manichanh because Manichanh teaches a metagenomic pipeline for analyzing microbial species in a fecal sample such as determining that Clostridium leptum phylogenetic group was significantly less abundant in CD patients than in healthy subjects [page 209 right col discussion]. Here, there is a reasonable expectation of success that combining Apte in view of metagenomic pipeline of Manichanh would yield a predictable method for analyzing fecal samples using metagenomic analysis to determine microbial abundances for assessing gut health of an individual.
Casen et al. (Casen) teach “a single numeric representation of the degree of dysbiosis, defined as the Dysbiosis Index (DI) [page 75 left col top para]. Casen teaches “each sample was processed using the finalized algorithm which converts data for each sample into a single integer, i.e. the DI, which represents the degree of dysbiosis based on bacterial abundance and profile within a sample relative to the established norm biotic profile.” [page 75 right col top para.], as in claim 1 generating a metric indicative of the overall health of the individual based on a result of comparing the aggregate presence of microbial species in the stool sample from the first pre-defined subset with the aggregate presence of microbial species in the stool sample from the second pre-defined subset.
Casen teaches a DI value of 2 was defined as class separation represented by the identified confidence limits; a DI of 2 or lower being the nondysbiotic region and a DI of 3 or higher being the dysbiotic region. Casen teaches the higher the DI above 2, the more the sample is considered to deviate from normobiosis, e.g. sample A with DI = 4 is farther away from the normobiotic reference cohort in the Euclidian space than sample B with DI = 3, thus A is more dysbiotic than B [page 75 left col top para]. Casen teaches ability to characterize the bacterial profiles both of normobiotic and dysbiotic patients may also help in evaluating the efficacy and further development of therapeutic approaches such as fecal microbiota transplantation (FMT), special diets and use of probiotics [page 72 right col top para], as in claim 1 if the metric falls within a first range of values indicative of a first level of poor overall health, providing a first set of dietary or behavioral recommendations for the individual and if the metric falls within a second range of values indicative of a second level of poor overall health, providing a second set of dietary or behavioral recommendations for the individual.
With respect to claim 4, the claim is rendered obvious because Casen teaches “The model algorithmically assesses fecal bacterial abundance and profile, and potential clinically relevant deviation in the microbiome from normobiosis [abstract]. Apte discloses a risk reference relative abundance range associated with the presence of and/or risk of one or more conditions [disclosure page 8 para 0063], as in claim 4, Here, Apte and Casen teach presence is an indication of the level of microbial abundance.
With respect to claim 6, the claim is rendered obvious because Apte discloses sequencing systems (e.g., sequencing platforms, etc.) for one or more of high-throughput sequencing [disclosure page 4 right col para 0039]. Casen teaches PCR of human fecal sample [page 74 left col sample preparation and detection].
Casen teach including different bacteria genes were analyzed to determine whether the microbes associated with the genes can be categorized as dysbiotic and/or non-dysbiotic [page 78 figures 5-6], as in claim 7. Here, the gene probes of Casen teach the inclusion of specific bacteria based on healthy or non-healthy gut biome (i.e., dysbiotic, non-dysbiotic).
With respect to claim 12, the claim is rendered obvious because Casen teaches comparing the top five probes sorted by the absolute relative difference between dysbiotic and non-dybiotic. Casen teaches that specific probes related to specific microbes belong to dysbiotic (i.e., unhealthy) and non-dysbiotic (i.e., healthy) categories [page 78 figures 5-6]. Here, it is obvious that a first pre-defined subset of healthy microbial species can be constructed from the microbial data of Casen to determine datasets of healthy microbial species to compare unhealthy samples.
With respect to claim 13, the claim is rendered obvious because Casen teaches comparing the top five probes sorted by the absolute relative difference between dysbiotic and non-dybiotic. Casen teaches that specific probes related to specific microbes belong to dysbiotic (i.e., unhealthy) and non-dysbiotic (i.e., healthy) categories [page 78 figures 5-6]. Here, it is obvious that a second pre-defined subset of unhealthy microbial species can be constructed from the microbial data of Casen to determine datasets of unhealthy microbial species to compare healthy samples.
With respect to claim 15, the claim is rendered obvious because Apte discloses a ratio feature describing a ratio between at least two microbiome features associated with the different taxonomic groups [disclosure page 9 para 0058]. Apte discloses “Additionally or alternatively, determining features can include determining features that describe the presence or absence of certain taxonomic groups of microorganisms, and/or ratios between exhibited taxonomic groups of microorganisms.” [disclosure page 7 right col para 0059]. Manichanh teaches using fecal sample [abstract]. Casen also teaches using stool sample [page 74 fig 2]. Here, Apte, Manichanh, and Casen make obvious that ratio is being determined between two datasets.
With respect to claim 16, the claim is rendered obvious because Apte discloses “determining one or more associations can include determining one or more parameters describing the one or more associations. Parameters describing the one or more associations can include any one or more of: effect size metrics ( e.g., coefficients of correlation, such as between abundances of one or more microorganism taxa and one or more microorganism-related conditions; z-scores, etc.), data enabling calculation of effect size metrics, mean, standard deviation, sample sizes, odds ratios, risk ratios, proportions of individuals in the control and study groups with and without the condition and/or any other suitable metrics, experimental parameters, confidence levels, sample characteristics, parameters associated with types of condition.” [disclosure page 9 left col para 0048]. Apte discloses “determining a significance index metric associated with characterization of the set of associations between the set of microorganism taxa and the at least one microorganism- related condition, based on the set of associations and the reference abundance ranges for the set of microorganism taxa.“ [Apte, claim 1]. Manichanh teaches using fecal sample [abstract]. Casen also teaches using stool sample [page 74 fig 2]. Here, Apte makes obvious using a ratio as an association between set of microbial taxa and one microbe-related condition for generating the significance metric.
Casen teaches a DI value of 2 was defined as class separation represented by the identified confidence limits; a DI of 2 or lower being the nondysbiotic region and a DI of 3 or higher being the dysbiotic region. Casen teaches the higher the DI above 2, the more the sample is considered to deviate from normobiosis, e.g. sample A with DI = 4 is farther away from the normobiotic reference cohort in the Euclidian space than sample B with DI = 3, thus A is more dysbiotic than B [page 75 left col top para], as in claim 18.
Casen teaches “The model algorithmically assesses fecal bacterial abundance and profile, and potential clinically relevant deviation in the microbiome from normobiosis. This model was tested in different samples from healthy volunteers and IBS and IBD patients (n = 330) to determine the ability to detect dysbiosis.” [abstract]. Casen teaches correlation between normalized GA-map signal data and MiSeq Illumina sequence data [page 79 table 4]. Casen teaches a DI of 3 or higher being the dysbiotic region. Casen teaches the higher the DI above 2, the more the sample is considered to deviate from normobiosis, e.g. sample A with DI = 4 is farther away from the normobiotic reference cohort in the Euclidian space than sample B with DI = 3, thus A is more dysbiotic than B [page 75 left col top para], as in claim 29. Here, the combination of statistical significance of the taxmomic group for 188 healthy and IBS samples and the DI score correlates the microbial taxa to dysbiosis (i.e., unhealthy) or poor overall health.
With respect to claim 32, the claim is rendered obvious because Apte discloses different machine learning models/algorithms for statistical analysis [disclosure page 15 left col para 0109-0110]. Apte discloses “training a machine learning model for classifying samples ( e.g., novel samples) from users as either healthy or presenting a microorganism-related condition. Apte discloses “applying the one or more selected machine learning models, such as using user features (e.g., derived a microorganism sequence dataset generated based on a user sample; etc.) as inputs, for classifying one or more samples as either healthy or presenting a microorganism-related condition, based on the probability output by the machine learning model. In a specific example, the machine learning model can output a probability of belonging to either group (e.g., healthy or microorganism-related condition) [disclosure pages 12-13 para 0092]. Apte discloses a figure that discloses dataset with different taxon’s associated with a user (i.e., you), healthy users, users with microorganism related condition 1 and users with microorganism related condition 2 [disclosure figure 9]. Casen teaches a DI of 3 or higher being the dysbiotic region. Casen teaches the higher the DI above 2, the more the sample is considered to deviate from normobiosis, e.g. sample A with DI