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
Acknowledgement is made of foreign priority to KR10-2020-0136235 filed October 20th, 2020 and the continuation of PCT/KR2021/012253 filed September 9th, 2021. The effective filing date is October 20th, 2020.
Status of Claims
Claims 1-16 are currently pending and are examined on the merits.
Information Disclosure Statement
The information disclosure statements filed March 9th 2023 and April 13th 2023 are acknowledged.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (claim 1-23 are drawn to a method; claim 24 is drawn to a system) (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites:
“…analyzing a mixture of a sample collected from a subject and a gut environment-like composition…”, which is a mental step, i.e. can be done with pen or paper
“…extracting a plurality of microbial data based on an analysis result of the mixture…”, mental step
“…selecting a microbe-related feature to be used for the machine learning model from the plurality of microbial data based on a predetermined feature selection algorithm…”, mental step
“…training the machine learning model by using the microbe-related feature to predict the presence or absence of colon polyps for each of the microbial data…”, which further limits claim 1, and
“…diagnosing the presence or absence of colon polyps based on an output value of the machine learning model …”, mental step
Claim 2 recites: “…number of features to be used for the machine learning model is 6 to 16…”, which further limits claim 1.
Claim 3 recites:
“…culturing the mixture in an anaerobic chamber for 18 hours to 24 hours under anaerobic conditions for 18 hours to 24 hours…”, which further limits claim 1, and
“…analyzing, by the diagnostic apparatus, a culture in which the mixture has been cultured…”, mental step.
Claim 4 recites:
“…analyzing a supernatant and a precipitate obtained by centrifugation of the culture…”, mental step
Claim 5 recites:
“…the microbial data includes at least one of the content, concentration and kind of substance contained in the culture, and a change in kind, concentration, content or diversity of bacteria included in microbiota, and the substance contained in the culture includes at least one of endotoxins, hydrogen sulfides, short-chain fatty acids (SCFAs) and microbiota-derived metabolites…”, which further limits claim 3
Claim 6 recites: “…the feature selection algorithm includes at least one of a Boruta algorithm and a recursive feature elimination (RFE) algorithm…”, which further limits claim 1
Claim 7 recites:
“…the machine learning model includes at least one of a logistic regression model, a glmnet model, a random forest model, a gradient boosting model and an extreme gradient boost (XGB) model…”, which further limits claim 1
Claim 8 recites:
“…wherein the microbe-related feature includes the content of at least one kind of microbes selected from genera belonging to the family Oscillospiraceae, the family Streptococcaceae, the family Enterococcaceae, the family Marinifilaceae, the family Lactobacillaceae, the family Clostridiaceae, the family Leuconostocaceae, the family Erysipelatoclostridiaceae and the family Lachnospiraceae.”, which further limits claim 1
Claim 9 recites:
“…the microbe-related feature includes the content of at least one kind of microbes selected from species belonging to the genus Enterococcus, the genus Odoribacter, the genus Streptococcus, the genus Lactobacillus, the genus Clostridium sensu stricto, the genus leuconostoc, the genus Erysipelatoclostridium and the genus Eisenbergiella…”, which further limits claim 1
Claim 10 recites:
“…a microbial data extraction unit that extracts a plurality of microbial data based on an analysis result of a mixture of a gut-derived substance collected from a subject and a gut environment-like composition…” , which further limits claim 10
“…a feature selection unit that selects a microbe-related feature to be used for the machine learning model from the plurality of microbial data based on a predetermined feature selection algorithm…”, which further limits claim 10
“…a training unit that trains the machine learning model by using the microbe-related feature to predict the presence or absence of colon polyps for each of the microbial data…”, which further limits claim 10,
“…a diagnosis unit that diagnoses colon polyps based on the presence or absence of colon polyps, which is an output value of the machine learning model, by inputting, into 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 and the gut environment-like composition, wherein the microbe-related feature includes the content of at least one kind of microbes selected from families belonging to the order Oscillospirales, the order Burkholderiales, the order Saccharimonadales, the order Lactobacillales, the order Bacteroidales, the order Clostridiales, the order Erysipelotrichales, the order Bacteroidales and the order Lachnospirales…”, which further limits claim 10
Claim 11 recites: “wherein number of features to be used for the machine learning model is 6 to 16.” Which further limits claim 10
Claim 12 recites: “wherein the microbial data includes at least one of the content, concentration and kind of substance contained in the culture wherein the mixture is cultured in an anaerobic chamber for 18 hours to 24 hours under anaerobic conditions for 18 hours to 24 hours, and a change in kind, concentration, content or diversity of bacteria included in microbiota, and the substance contained in the culture includes at least one of endotoxins, hydrogen sulfides, short-chain fatty acids (SCFAs) and microbiota-derived metabolites.”, which further limits claim 10
Claim 13 recites: “wherein the feature selection algorithm includes at least one of a Boruta algorithm and a recursive feature elimination (RFE) algorithm,” which further limits claim 10.
Claim 14 recites: “wherein the machine learning model includes at least one of a logistic regression model, a glmnet model, a random forest model, a gradient boosting model and an extreme gradient boost (XGB) model.”, which further limits claim 10.
Claim 15 recites: “wherein the microbe-related feature includes the content of at least one kind of microbes selected from genera belonging to the family Oscillospiraceae, the family Streptococcaceae, the family Enterococcaceae, the family Marinifilaceae, the family Lactobacillaceae, the family Clostridiaceae, the family Leuconostocaceae, the family Erysipelatoclostridiaceae and the family Lachnospiraceae.”, which further limits claim 10.
Claim 16 recites: “wherein the microbe-related feature includes the content of at least one kind of microbes selected from species belonging to the genus Enterococcus, the genus Odoribacter, the genus Streptococcus, the genus Lactobacillus, the genus Clostridium sensu stricto, the genus leuconostoc, the genus Erysipelatoclostridium and the genus Eisenbergiella.” Which further limits claim 10.
The claims recite an abstract idea of analyzing a biological sample (See MPEP 2106.07(a)).
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas.
While claim 1 recites performing some aspects of the analysis using a “model”, there are no additional limitations that indicate that this model requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claim(s) 1-16 recite(s) an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
Claim 1 recites: “…training the machine learning model by using the microbe-related feature to predict the presence or absence of colon polyps for each of the microbial data…”
Claim 2 and claim 11 recite: “…number of features to be used for the machine learning model is 6 to 16…”
Claim 3 recites: “…culturing the mixture in an anaerobic chamber for 18 hours to 24 hours under anaerobic conditions for 18 hours to 24 hours…”
Claim 4 recites: “…analyzing a supernatant and a precipitate obtained by centrifugation of the culture…”
Claim 5 recites: ““…the microbial data includes at least one of the content, concentration and kind of substance contained in the culture, and a change in kind, concentration, content or diversity of bacteria included in microbiota, and the substance contained in the culture includes at least one of endotoxins, hydrogen sulfides, short-chain fatty acids (SCFAs) and microbiota-derived metabolites…”
Claim 6 recites: “…the feature selection algorithm includes at least one of a Boruta algorithm and a recursive feature elimination (RFE) algorithm…”
Claim 7 recites: “…the machine learning model includes at least one of a logistic regression model, a glmnet model, a random forest model, a gradient boosting model and an extreme gradient boost (XGB) model…”
Claim 8 and claim 15 recite: “…wherein the microbe-related feature includes the content of at least one kind of microbes selected from genera belonging to the family Oscillospiraceae, the family Streptococcaceae, the family Enterococcaceae, the family Marinifilaceae, the family Lactobacillaceae, the family Clostridiaceae, the family Leuconostocaceae, the family Erysipelatoclostridiaceae and the family Lachnospiraceae.”
Claim 9 and claim 16 recite: “…the microbe-related feature includes the content of at least one kind of microbes selected from species belonging to the genus Enterococcus, the genus Odoribacter, the genus Streptococcus, the genus Lactobacillus, the genus Clostridium sensu stricto, the genus leuconostoc, the genus Erysipelatoclostridium and the genus Eisenbergiella…”
Claim 10 recites:
“…a microbial data extraction unit that extracts a plurality of microbial data based on an analysis result of a mixture of a gut-derived substance collected from a subject and a gut environment-like composition…”
“…a feature selection unit that selects a microbe-related feature to be used for the machine learning model from the plurality of microbial data based on a predetermined feature selection algorithm…”
“…a training unit that trains the machine learning model by using the microbe-related feature to predict the presence or absence of colon polyps for each of the microbial data…”,
“…a diagnosis unit that diagnoses colon polyps based on the presence or absence of colon polyps, which is an output value of the machine learning model, by inputting, into 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 and the gut environment-like composition, wherein the microbe-related feature includes the content of at least one kind of microbes selected from families belonging to the order Oscillospirales, the order Burkholderiales, the order Saccharimonadales, the order Lactobacillales, the order Bacteroidales, the order Clostridiales, the order Erysipelotrichales, the order Bacteroidales and the order Lachnospirales…”,
Claim 12 recites: “wherein the microbial data includes at least one of the content, concentration and kind of substance contained in the culture wherein the mixture is cultured in an anaerobic chamber for 18 hours to 24 hours under anaerobic conditions for 18 hours to 24 hours, and a change in kind, concentration, content or diversity of bacteria included in microbiota, and the substance contained in the culture includes at least one of endotoxins, hydrogen sulfides, short-chain fatty acids (SCFAs) and microbiota-derived metabolites.”
Claim 13 recites: “wherein the feature selection algorithm includes at least one of a Boruta algorithm and a recursive feature elimination (RFE) algorithm.”
Claim 14 recites: “wherein the machine learning model includes at least one of a logistic regression model, a glmnet model, a random forest model, a gradient boosting model and an extreme gradient boost (XGB) model.”
There are no limitations that indicate that the claimed analysis engine or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. As such, claims 1-11, 17-24 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-16 is/are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 7-10, and 14-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yaping Liu et al (WO2019191649A1).
Claim limitations will be addressed in italics.
Regarding claims 1 and 10, Liu et al. discloses Systems, media, methods, and kits disclosed herein can be used to analyze human microbiota for the detection of a condition (e.g., a disease or condition) (Abstract). Liu et al. further discloses In some embodiments, a method of the present disclosure can be used to diagnose a cancer, inclusive of adenomatous polyps (para. 00241, re: clm. 1, …A method of diagnosing the presence or absence of colon polyps…).
Liu et al. further discloses the systems, media, methods, and kits disclosed herein can utilize machine learning algorithms to analyze samples with high accuracy, and using a computer (Abstract, Spec, para. 0043, 0007, 0032, 0033, re: clm. 1, …by using a machine learning model, which is performed by a diagnostic apparatus…, clm. 10, … An apparatus of diagnosing the presence or absence of colon polyps by using a machine learning model…)
Liu et al. further discloses that the present disclosure provides non-invasive systems and methods for detecting gut microbiota and detecting communities of microbiota and for diagnosing diseases such as cancer, using a biological sample obtained from a subject (Spec, para. 0079, 0083, re: clm. 1, …analyzing a mixture of a sample collected from a subject and a gut environment-like composition…).
Liu et al. further discloses methods comprising high-throughput sequencing of a cell -free nucleic acid sample from a subject, followed by bioinformatics analysis to determine the presence and prevalence of microbial sequences, and machine learning is used to derive features from said microbial sequences (Spec, para. 00119, 00130-00132, re: clm. 1, … extracting a plurality of microbial data based on an analysis result of the mixture; selecting a microbe-related feature to be used for the machine learning model from the plurality of microbial data based on a predetermined feature selection algorithm…, clm. 10, … a microbial data extraction unit that extracts a plurality of microbial data based on an analysis result of a mixture of a gut-derived substance collected from a subject and a gut environment-like composition… a feature selection unit that selects a microbe-related feature to be used for the machine learning model from the plurality of microbial data based on a predetermined feature selection algorithm…).
Liu et al. further discloses method of detecting presence of cancer in an individual comprising: (a) mapping a plurality of sequence reads obtained from sequencing a cell-free nucleic acid sample to a reference nucleic acid sequence; (b) separating sequence reads that do not map to the reference nucleic acid sequence, thereby providing presumed microbiome sequence reads and (d) applying a predictive model to the actual microbiome sequence reads to classify the subject to detect the presence of cancer in the subject (Spec, para. 0049). Liu et al. continues, describing training a machine learning model on disease-related features to predict disease characteristics in a sample, inclusive of polyps, and reads on diagnosing colon polyps based on their presence (as their presence would denote a feature classified by the algorithm) (Spec, para. 00154, 00241, re: clm. 1, …training the machine learning model by using the microbe-related feature to predict the presence or absence of colon polyps for each of the microbial data; and diagnosing the presence or absence of colon polyps based on an output value of the machine learning model by inputting, into the trained machine learning model, the microbial data extracted based on the analysis result of the mixture of the sample collected from the subject and the gut environment-like composition…, clm. 10, …a training unit that trains the machine learning model by using the microbe-related feature to predict the presence or absence of colon polyps for each of the microbial data; and a diagnosis unit that diagnoses colon polyps based on the presence or absence of colon polyps, which is an output value of the machine learning model, by inputting, into 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 and the gut environment-like composition).
Liu et al. further discloses microbe-related features extracted from samples can be from Streptococcus (genus) and/or Streptococcaceae (family) (Spec, para. 0011-0012, re: clm. 1, 10, … wherein the microbe-related feature includes the content of at least one kind of microbes selected from families belonging to the order Oscillospirales, the order Burkholderiales, the order Saccharimonadales, the order Lactobacillales, the order Bacteroidales, the order Clostridiales, the order Erysipelotrichales, the order Bacteroidales and the order Lachnospirales.). As Liu et al. discloses a method of diagnosis the presence of colon polyps, Liu et al. anticipates claim 1.
Regarding claim 2, Liu et al. discloses that the trained algorithm may be trained with at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training samples, which reads on using 6 to 16 machine learning features for the machine learning model (Spec, para. 00154, re: clm. 2, …wherein number of features to be used for the machine learning model is 6 to 16…). Liu et al. anticipates claim 2.
Regarding claims 7 and 14, Liu et al. discloses a logistic regression model (Spec, para. 0033, re: clm. 7, 14 … wherein the machine learning model includes at least one of a logistic regression model, a glmnet model, a random forest model, a gradient boosting model and an extreme gradient boost (XGB) model…). Liu et al. anticipates claim 7.
Regarding claims 8, 9, 15, and 16, Liu et al. discloses microbe-related features extracted from samples can be from Streptococcus (genus) and/or Streptococcaceae (family) (Spec, para. 0011-0012, re: clm. 8, 15, wherein the microbe-related feature includes the content of at least one kind of microbes selected from… the family Streptococcaceae…clm. 9, 16 … wherein the microbe-related feature includes the content of at least one kind of microbes selected from species belonging to…the genus Streptococcus…). Liu et al. anticipates claims 8, 9, 15, and 16.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. as applied to claims 1, 2, 7-10, and 14-16 above, in view of Ola Winqvist et al (US20190389896A1)
Liu et al. is applied to claims 1, 2, 7-10, and 14-16 above.
Regarding claim 3, Winqvist et al. discloses a method of assessing antibacterial activity in which bacterial strains are cultured in an anaerobic chamber for 18 hours (Spec, para. 0190, re: clm. 3, … wherein the analyzing a mixture includes:, culturing the mixture in an anaerobic chamber for 18 hours to 24 hours under anaerobic conditions for 18 hours to 24 hours; and analyzing, by the diagnostic apparatus, a culture in which the mixture has been cultured.)
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard of obviousness to Liu et al. and Winqvist et al., the examiner concludes that the combination of Winqvist et al.’s culture method, with the systems, media, methods, and kits disclosed to analyze human microbiota for the detection of a condition of Liu et al., represents
a combination of known elements which would have yielded the predictable result of systems, media, methods, and kits disclosed to analyze human microbiota for the detection of a condition with an 18 hour anaerobic culture method with the requisite specificity and selectivity. The use of the culture method as disclosed by Winqvist et al. in this combination would have further served to achieve the predictable result of more efficient microbiota culturing for later feature derivation via Liu et al’s method. Such a combination is merely a "predictable use of prior art elements according to their established functions." KSR Int’l 7, 127 S. Ct. at 1740.
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. and in view of Winqvist et al. as applied to claims 1-3, 7-10, and 14-16 above, and in view of Tramontano et al ( EP3520799A1).
Liu et al. in view of Winqvist et al. is applied to claims 1-3, 7-10, and 14-16 above.
Regarding claim 4, Tramontano et al. discloses an In-vitro model of the human gut microbiome and uses thereof in the analysis of the impact of xenobiotics, in which bacteria were grown 48h anaerobically, split in half, subsequently either the supernatant or the total fraction was ACN:MethOH extracted and the concentration was measured with UV-UPLC detection (Spec, Fig. 16). Tramontano et al. also discloses characterization of bacterial growth in which a bacterial medium precipitate was removed with centrifugation (Spec para. 0162, re: clm. 4, …wherein the analyzing a culture includes: analyzing a supernatant and a precipitate obtained by centrifugation of the culture…)/
Applying the KSR standard of obviousness to Liu et al., Winqvist et al., and Tramontano et al., the examiner concludes that the combination of Winqvist et al.’s culture method, Tramontano et al.’s supernatant and precipitate processing with the systems, media, methods, and kits disclosed to analyze human microbiota for the detection of a condition of Liu et al., represents
a combination of known elements which would have yielded the predictable result of systems, media, methods, and kits disclosed to analyze human microbiota for the detection of a condition with an 18 hour anaerobic culture method and supernatant generation, processing and analysis which provides the requisite specificity and selectivity. The use of the culture method as disclosed by Winqvist et al. and the processing of the culture as taught by Tramontano et al. in this combination would have further served to achieve the predictable result of more efficient microbiota culturing for later feature derivation via Liu et al’s method. Such a combination is merely a "predictable use of prior art elements according to their established functions." KSR Int’l 7, 127 S. Ct. at 1740.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. in view of Winqvist et al. and in view of Tramontano et al. as applied to claims 1-4, 7-10, and 14-16 above, and in view of Drouillard et al (US11492587B2).
Liu et al. in view of Winqvist et al. and in view of Tramontano et al. is applied to claims 1-4, 7-10, and 14-16 above
Regarding claim 5, Drouillard et al. discloses microbial cells, methods of producing the same, and uses thereof, and further discloses measurements of endotoxin concentration levels from bacteria (Fig. 14, re: clm. 5, … herein the microbial data includes at least one of the content, concentration and kind of substance contained in the culture, and a change in kind, concentration, content or diversity of bacteria included in microbiota, and the substance contained in the culture includes at least one of endotoxins…).
Applying the KSR standard of obviousness to Liu et al., Winqvist et al., Tramontano et al., and Drouillard et al., the examiner concludes that the combination of Winqvist et al.’s culture method, Tramontano et al.’s supernatant and precipitate processing, and Drouillard et al’s endotoxin concentration measurement with the systems, media, methods, and kits disclosed to analyze human microbiota for the detection of a condition of Liu et al., represents a combination of known elements which would have yielded the predictable result of systems, media, methods, and kits disclosed to analyze human microbiota for the detection of a condition with an 18 hour anaerobic culture method , supernatant generation, and endotoxin concentration measurement processing and analysis which provides the requisite specificity and selectivity. The use of the culture method as disclosed by Winqvist et al., the processing of the culture as taught by Tramontano et al, and the measurement of endotoxin levels from a bacterial culture concentrate as taught by Drouillard et al. in this combination would have further served to achieve the predictable result of more efficient microbiota culturing for later feature derivation via Liu et al’s method. Such a combination is merely a "predictable use of prior art elements according to their established functions." KSR Int’l 7, 127 S. Ct. at 1740.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. as applied to claims 1-5, 7-10, and 14-16 above, in view of Besner et al (WO2020086487A1) and in view of Mouliere et al (WO2020094775A1).
Liu et al. is applied to claims 1-5, 7-10, and 14-16 above.
Besner et al. discloses Compositions and methods for preventing and treating antibiotic induced pathologies using probiotics in the biofilm state, and further discloses that the relative abundances of bacterial genera (and when possible putative species) are analyzed by the machine learning algorithm Random Forest (RF), alongside Boruta feature selection, to derive a list of bacteria that best characterize samples between treatment groups (Spec, para. 0162, re: clm. 6, … wherein the feature selection algorithm includes at least one of a Boruta algorithm).
Besner et al. and Liu et al. do not disclose a recursive feature elimination algorithm
Mouliere et al. discloses a computer-implemented method for detecting variant nucleic acid from a cell-free nucleic acid- containing sample, in which logistic regression (LR) and random forest (RF) algorithms paired with recursive feature selection was used in order to identify the best predictor variables (Spec, “Classification analysis” section, re: clm. 6, … a recursive feature elimination (RFE) algorithm…).
Applying the KSR standard of obviousness to Liu et al, Besner et al. and Mouliere et al, the examiner concludes that the combination of Besner et al.’s Boruta algortithm and Mouliere et al.’s RFE algortithm with the systems, media, methods, and kits disclosed to analyze human microbiota for the detection of a condition of Liu et al. represents Some Teaching, suggestion or motivation in the prior art that would have lead one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
One of ordinary skill in the art would be motivated to combine the teachings of Liu et al, Besner et al. and Mouliere et al. because the combination would result in a stronger method of analyzing human microbiota for the detection of a disease or condition. One of ordinary skill in the art would have had a reasonable expectation of success because all three arts disclose methods to analyze bacterial nucleotide sequences in relation to disease. Therefore, it would have been prima facie obvious for one of ordinary skill at the time of filing to combine the aforementioned arts to analyze microbial sequences to predict disease in a manner reading on the current invention, absent evidence to the contrary.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T STUBBS whose telephone number is (571)272-0340. The examiner can normally be reached M-F 8-5 EST.
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/J.T.S./ Examiner, Art Unit 1686
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686
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