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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in the instant Application No. 18/458,297, filed on 8/30/2026. As such the affective filing date for claims 1-14 is 3/21/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/30/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
Claims 1-14 are pending.
Claims 1-14 are rejected.
Specification
The use of the term Bluetooth, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “microbial data extraction unit that extracts multiple microbial data”, “feature selection unit that selects microbe-related features”, “training unit that trains”, and “diagnosis unit that inputs” in claim 9. These claim limitations are not described within the specification beyond the operations they perform (i.e., feature selection)” – Paragraph [0077] of Specification, “microbial data extraction unit 100 may extract multiple microbial data” – Paragraph [0047] of Specification, “The training unit 120 may train the machine learning model with the microbe-related features” – Paragraph [0084] of Specification, and “The diagnosis unit 130 may diagnose hyperglycemia” – Paragraph [0087]-[0088] of Specification) and within Figure 1 are shown to be connected by a Bus which is a communication system that transfers data between components within a, or between, computer(s). As such are being interpreted as conventional computer elements in the furtherance of compact prosecution.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 9-14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 9 recites the following limitations, “microbial data extraction unit that extracts multiple microbial data”, “feature selection unit that selects microbe-related features”, “training unit that trains”, and “diagnosis unit that inputs”, yet the specification and claim fails to recite sufficient structure so as to describe in sufficient detail what the units are or how they operate, only defining them by what they do (i.e. “feature selection unit 110 may perform selection (i.e., feature selection)” – Paragraph [0077] of Specification, “microbial data extraction unit 100 may extract multiple microbial data” – Paragraph [0047] of Specification, “The training unit 120 may train the machine learning model with the microbe-related features” – Paragraph [0084] of Specification, and “The diagnosis unit 130 may diagnose hyperglycemia” – Paragraph [0087]-[0088] of Specification). Furthermore, the drawings include Figure 1 with the specified structures being connected by a Bus which is a communication system that transfers data between components within a, or between, computer(s). As such it is impossible to know whether the recited units are components of a singular computer, multiple computers connected to each other, or merely signals being transmitted between the various computer elements. Thus, the specification does not reasonably convey to a person of ordinary skill in the art that the inventor was in possession of this specific claimed attachment method at the time of filing. As such claim 9, and all dependent claims as they fail to cure said deficiencies, are rejected under 35 U.S.C. 112(a) for failing to properly describe the invention.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method and apparatus for diagnosing hyperglycemia. The judicial exception is not integrated into a practical application because while claims 1-14 attempt to integrate the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea, or it is insignificant extra solution activity and simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a method (claims 1-8) and apparatus (claims 9-14).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claims 1 and 9: Analyzing a mixture, extracting microbial data based on analysis, and selecting microbe-related features are processes of comparing/contrasting, identifying, selecting, and calculating information that can be done via pen and paper or withing the human mind and are therefore abstract ideas, specifically mental processes. The method correlates between microbial data from a subject’s gut derived substance and hyperglycemia, which is a law of nature. The microbe-related features including the specified information is merely further limiting the data itself which is an abstract idea, specifically a mental process. Selecting microbe-related features based on a Boruta algorithm or recursive feature elimination algorithm is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claims 2 and 10: The number of features being used is 6-10, is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 3: Analyzing a culture is a process of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 4: Analyzing the supernatant and the precipitate is a process of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 5: The microbial data including the specified information is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 6 and 12: The machine learning model including the specified models merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 7 and 13: The microbe-related features including the specified information is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 8 and 14: The microbe-related features comprising the specified information is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 11: The microbial data comprising the specified data is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claims 1 and 9: Training the machine learning model and inputting the microbial data are insignificant extra solution activities, specifically mere data gathering and necessary data outputting (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827- 28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claim 3: Culturing the mixture for 18-24 hours under anaerobic conditions is an insignificant extra solution activity, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827- 28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claim 4: Centrifuging the culture is an insignificant extra solution activity, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827- 28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional, nonspecific, or insignificant extra solution activity. These additional elements include:
The additional elements of training the machine learning model, inputting the microbial data, culturing the mixture for 18-24 hours under anaerobic conditions, and centrifuging the culture are all insignificant extra solution activities, specifically mere data gathering and necessary data outputting (See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-14, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, and 5-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zeevi et al. (Cell (2015) 1079-1094) and Darst et al. (BMC genetics (2018) 1-6).
Claim 1 is directed to a method for diagnosing the presence or absence of hyperglycemia using machine learning.
Claim 9 is directed to an apparatus for diagnosing the presence or absence of hyperglycemia using machine learning.
Zeevi et al. teaches in the abstract “Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals”, reading on a method for diagnosing the presence or absence of hyperglycemia by using a machine learning model. Zeevi et al. teaches on page 1083, column 1, paragraph 2 “Prior to CGM connection, a comprehensive profile was collected from each participant, including: food frequency, lifestyle, and medical background questionnaires; anthropometric measures (e.g., height, hip circumference); a panel of blood tests; and a single stool sample, used for microbiota profiling by both 16S rRNA and metagenomic sequencing…The first two principal coordinates show some distinction between our cohort and the other cohorts, but when HMP samples from other body sites are added to the PCoA, stool samples from all three cohorts cluster together and separate from the rest, indicative of overall similarity in the gut microbiota composition of individuals from these three distinct geographical regions”, reading on a process of analyzing a mixture of a gut-derived substance collected from a subject and a gut environment-like composition and a process of extracting multiple microbial data based on an analysis result of the mixture. Zeevi et al. teaches on page 1084, column 2, paragraph 3 “The features within each tree are selected by an inference procedure from a pool of 137 features representing meal content (e.g., energy, macronutrients, micronutrients); daily activity (e.g., meals, exercises, sleep times); blood parameters (e.g., HbA1c%, HDL cholesterol); CGM-derived features; questionnaires; and microbiome features”, reading on a process of selecting microbe-related features to be used in the machine learning model from the multiple microbial data. Zeevi et al. teaches on page 1084, column 2, paragraph 2 “In the first, discovery phase, the algorithm was developed on the main cohort of 800 participants, and performance was evaluated using a standard leave-one-out cross validation scheme, whereby PPGRs of each participant were predicted using a model trained on the data of all other participants…Trees are inferred sequentially, with each tree trained on the residual of all previous trees and making a small contribution to the overall prediction”, and in the abstract “We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals”, reading on a process of training the machine learning model with the microbe-related features to predict whether hyperglycemia is present for each of the microbial data. Zeevi et al. teaches in the abstract “We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals”, on page 1091, column 1, paragraph 3 “Our algorithm takes as input a comprehensive clinical and microbiome profile and employs a data-driven unbiased approach to infer the major factors that are predictive of PPGRs”, reading on a process of inputting, to the trained machine learning model, the microbial data extracted based on the analysis result of the mixture of the gut-derived substance collected from the subject to be tested and the gut environment-like composition and determining whether hyperglycemia is present based on an output value of the machine learning model. Zeevi et al. teaches on page 1089, column 1, paragraph 6 “TIIDM has been associated with low levels of Roseburia inulinivorans, Eubacterium eligens, and Bacteroides vulgatus”, reading on wherein the microbe-related features include the amount of one or more microbes selected from families included in orders, Oscillospirales, Lachnospirales, Lactobacillales, and Peptostreptococcales-Tissierellales.
Zeevi et al. does not teach the use of a Boruta algorithm or a recursive feature elimination (RFE) algorithm.
Darst et al. teaches in the abstract “Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact its ability to identify strong predictors. The Random Forest-Recursive Feature Elimination algorithm (RF-RFE) mitigates this problem in smaller data sets…We integrated 202,919 genotypes and 153,422 methylation sites in 680 individuals, and compared the abilities of RF and RF-RFE to detect simulated causal associations, which included simulated genotype–methylation interactions, between these variables and triglyceride levels. Results show that RF was able to identify strong causal variables with a few highly correlated variables”, reading on the use of a Boruta algorithm or a recursive feature elimination (RFE) algorithm.
It would have been obvious at the time of first filing to have modified the teachings of Zeevi et al. for the use of microbiome gut data in machine learning to predict hyperglycemia, with the teachings of Darst et al. for the use of recursive feature elimination in machine learning, as the latter is explicitly using a similar model, random forest, as the former, gradient boosted decision trees, and additionally finds “…that RF was able to identify strong causal variables with a few highly correlated variables…”. One would have had a reasonable expectation of success given that the teachings of Darst et al. are directly applicable to a similar model being used in Zeevi et al. and would merely be a substitution of one known method for another, both with predictable results. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 2 is directed to the method of claim 1 but further specifies that the number of features to be used in the model is to be 6-10.
Claim 10 is directed to the apparatus of claim 9 but further specifies that the number of features to be used in the model is to be 6-10.
Darst et al. teaches and applicant claims the use of, recursive feature selection, a feature selection model which removes redundant/correlated features to improve model performance. It would be prima facie obvious to therefore optimize the model for best performance using feature selection methods such as those described and to arrive at the specified number of features, thereby reading on wherein the number of features to be used in the machine learning model is 6 to 10.
Claim 5 is directed to the method of claim 1 but further specifies that the microbial data include at least one of the specified pieces of information.
Claim 11 is directed to the apparatus of claim 9 but further specifies that the microbial data include at least one of the specified pieces of information.
Zeevi et al. teaches on page 4 of the S1 supplemental information, in paragraph 3 under Predictor Features “Microbiome features - relative abundances…”, reading on wherein the microbial data include at least one of the amount, concentration, and kind of one or more of endotoxins, hydrogen sulfides, short-chain fatty acids (SCFAs) and microbiota-derived metabolites contained in the culture, and a change in kind, concentration, amount or diversity of bacteria included in the microbiota.
Claim 6 is directed to the method of claim 1 but further specifies the use of one of the specified models.
Claim 12 is directed to the apparatus of claim 9 but further specifies the use of one of the specified models.
Zeevi et al. teaches 1084, column 2, paragraph 3 “Given non-linear relationships between PPGRs and the different factors, we devised a model based on gradient boosting regression”, reading on wherein the machine learning model includes at least one of a logistic regression model, a generalized linear (GLM) model, a random forest model, a gradient boosting model, and an extreme gradient boosting (XGB) model.
Claim 7 is directed to the method of claim 1 but further specifies that the microbes be selected from the specified families.
Claim 13 is directed to the apparatus of claim 9 but further specifies that the microbes be selected from the specified families.
Zeevi et al. teaches on page 1089, column 1, paragraph 6 “TIIDM has been associated with low levels of Roseburia inulinivorans, Eubacterium eligens, and Bacteroides vulgatus”, reading on wherein the microbe-related features include the amount of one or more microbes selected from genera included in families, Ruminococcaceae, Lachnospiraceae, Leuconostocaceae, and Peptostreptococcaceae.
Claim 8 is directed to the method of claim 1 but further specifies that the microbes be selected from the specified genera.
Claim 14 is directed to the apparatus of claim 9 but further specifies that the microbes be selected from the specified genera.
Zeevi et al. teaches on page 1089, column 1, paragraph 6 “TIIDM has been associated with low levels of Roseburia inulinivorans, Eubacterium eligens, and Bacteroides vulgatus”, reading on wherein the microbe-related features include the amount of one or more microbes selected from species included in genera, Subdoligranulum, Ruminococcus, Weissella, and Intestinibacter.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Zeevi et al. (Cell (2015) 1079-1094) and Darst et al. (BMC genetics (2018) 1-6) as applied to claims 1-2, and 5-14 above, and further in view of Miyajima et al. (PloS one (2011) 1-5).
Claim 3 is directed to the method of claim 1 but further specifies the culturing of the sample in anaerobic conditions for 18-24 hours.
Zeevie et al. and Darst et al. teach the method of claim 1 as previously described.
Zeevie et al. and Darst et al. do not teach the culturing of the sample in anaerobic conditions for 18-24 hours.
Miyajima et al. teaches on page 2, column 2, paragraph 3 “The isolate was inoculated into a 15 ml Brain Heart Infusion broth and cooked meat medium (Southern Group Laboratory Ltd, Corby, UK) and incubated anaerobically for 18–24 h at 37uC. Cultures were then centrifuged and the filtered supernatant used to test for the presence of toxins A and/or B using a TOX A/B II ELISA kit”, reading on wherein the process of analyzing a mixture includes: a process of culturing the mixture for 18 to 24 hours under anaerobic conditions; and a process of analyzing a culture in which the mixture has been cultured.
It would have been obvious at the time of first filing to have modified the teachings of Zeevie et al. and Darst et al. for the method of claim 1, with the teachings of Miyajima et al. for the specific culturing methods as the latter is working with fecal samples to culture gut microbes and is working with conventional methods within the art (freely available kits). One would have had a reasonable expectation of success given the use of such kits on similar tissue samples, which would merely be a substitution of one known method for another, both with predictable results. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 4 is directed to the method of claim 3, and thus claim 1, but further specifies centrifuging the sample.
Zeevie et al. and Darst et al. teach the method of claim 1 as previously described.
Zeevie et al. and Darst et al. do not teach the centrifuging of the sample.
Miyajima et al. teaches on page 2, column 2, paragraph 3 “The isolate was inoculated into a 15 ml Brain Heart Infusion broth and cooked meat medium (Southern Group Laboratory Ltd, Corby, UK) and incubated anaerobically for 18–24 h at 37uC. Cultures were then centrifuged and the filtered supernatant used to test for the presence of toxins A and/or B using a TOX A/B II ELISA kit”, reading on wherein the process of analyzing a culture includes: a process of centrifuging the culture to separate a supernatant and a precipitate and analyzing the supernatant and the precipitate.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEENAN NEIL ANDERSON-FEARS whose telephone number is (571)272-0108. The examiner can normally be reached M-Th, alternate F, 8-5.
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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