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 .
Response to Arguments
Applicant’s arguments filed 03/19/2026 have been fully considered but are not persuasive or are moot in view of new grounds of rejection.
Applicant argues, "the systems and methods discussed herein provide a multi-stage machine learning system that provides more efficient prediction of health or performance scores or metrics at reduced computational cost relative to analysis of a full set of metabolic data….The claims reflect these improvements in at least two different ways. The first is through the use of the shadow generator and the second is through the filtering engine….For at least the above reasons, Applicant respectfully submits that the independent claims integrate any otherwise judicially excepted subject matter that is recited into a practical application”.
Examiner respectfully disagrees.
Regarding the limitations directed to
“the metabolite analyzer comprising an application, server, service, daemon, routine, or other executable logic,
wherein the metabolite analyzer includes
a classifier,
a shadow data generator, and
a filtering engine,
a time-of-flight mass spectrometer, a gas chromatograph or a liquid chromatograph,”
see Rose et al. (US 2020/0227166), which discloses performing clinical assessments [0009] by measuring a panel of analytes [0009], and teaches
a metabolite analyzer ([0011]: analytes may comprise of metabolites; [0164]: models may be used to analyze analyte data) comprising an
application,
server,
service,
daemon,
routine, or
other executable logic [0164],
wherein the metabolite analyzer includes
a classifier ([0164]: random forest is a classifier; [0330]),
a shadow data generator
([0232]: random shuffling of data is interpreted as a shadow data generator, as also evidenced by Applicant’s spec “a second set of “shadow” training data 122 may be generated by randomizing or shuffling the training data 120 (e.g. for each sample, assigning a random measurement value to each metabolite in some implementations; or by randomly shuffling” [0056]; [0212]), and
a filtering engine [0301],
a time-of-flight mass spectrometer [0246], a gas chromatograph [0226] or a liquid chromatograph [0226].
Thus, the limitations directed to
“the metabolite analyzer comprising an application, server, service, daemon, routine, or other executable logic,
wherein the metabolite analyzer includes
a classifier,
a shadow data generator, and
a filtering engine,
a time-of-flight mass spectrometer, a gas chromatograph or a liquid chromatograph,”
are well-understood, routine, and conventional, as evidenced by the reference above.
The additional elements in the claim amount to no more than insignificant extra solution activity and generic computer components, and there is nothing in the claims which integrate the judicial exception into a practical application.
Therefore, none of the claims 40-59 amount to significantly more than the abstract idea itself.
Applicant argues, that Apte fails to disclose a "filtering engine".
Examiner respectfully disagrees. Specifically, Apte discloses a filtering engine ([0059]: Block S130 may include filtering).
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 40-59 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically an abstract idea without significantly more.
Step 1:
Independent claims 40 and 53 are directed to a method and a system of identifying a set of predictor metabolites which are predictive of a state of a subject being an animal subject, respectively. Thus, they are directed to statutory categories of invention.
Step 2A, Prong 1:
Claims 40 and 53 recites the following claim limitations which are directed to abstract ideas, specifically mental processes and mathematical concepts (see MPEP § 2106.04(a)):
Claim 40:
applying… a feature selection process to the plurality of initial data sets to select and thereby identify a subset of the plurality of metabolites of which subset the concentrations are a statistically significant predictor of the state according to the label in that with the computing device a metabolite analyzer is executed (mental process – doctor can identify a subset of the plurality of metabolites which indicate a statistically significant predictor of the state according to the label),
wherein by executing the classifier, for each metabolite of the initial data sets, importance scores for the importance of the respective metabolite for contributing to a corresponding health or performance score or metric are calculated (mathematical concept – mathematical equations and relationships, see Applicant’s specification “the animal subjects may be classified via a feature selection process, such as a random forest classifier 124 or similar classifier, to generate importance or significance values or feature importance scores 126” [0055]),
wherein by executing the shadow data generator, shadow data sets are generated from the initial data sets by random reshuffling the measurements of the concentrations the metabolites and/or health or performance scores or metrics or by generating new random values (mathematical concept – mathematical equations and relationships, see Applicant’s specification “shadow training data generated from randomization or shuffling of training data” [0055] and “shadow training data 122 may be generated by creating random metabolite concentration or measurement values and/or scores or metrics (e.g. creating new “fake” or shadow samples for the shadow training set). In some implementations, a mix of shuffled and randomly generated data may be utilized” [0063]),
wherein by executing the classifier, for each metabolite of the shadow data sets, importance scores for the importance of the respective metabolite for contributing to a corresponding health or performance score or metric are calculated (mathematical concept – mathematical equations and relationships, see above),
…the importance scores from the initial data sets are compared to the importances scores from the shadow data sets to identify scores above a threshold and filter a subset of metabolites in that for each metabolite, for the respective metabolite (mental process – person can compare importance scores, identify a threshold, and filter metabolites that are above the threshold),
the importance score of the initial data sets is selected and compared to the importance score for the respective metabolite of the shadow data sets (mental process- person can select and compare importance scores),
wherein, if the importance score for the metabolite of the initial data sets exceeds the importance score of the shadow data sets, the respective metabolite is added to the filtered data set and otherwise excluded from the filtered data set (mental process – person can see if the initial data sets exceed the importance score of the shadow data sets and exclude data as needed),
Claim 53: see claim 40 above
These limitations, under their broadest reasonable interpretation, cover concepts that can be practically performed in the human mind or are mathematical concepts (mathematical relationships and equations).
Therefore, the claim limitations fall within the 'mental processes' and ‘mathematical concepts’ grouping of abstract ideas.
Step 2A, Prong 2:
Claims 40 and 53 recite the following additional elements:
Claim 40:
obtaining a plurality of initial data sets, each of the plurality of initial data sets comprising measurement data comprising an indication of a concentration of each of a plurality of metabolites in a sample of a microbiome of a respective subject, and a label at least in part characterizing the state of the subject, the label being a health or performance score or metric,
wherein the plurality of initial data sets is obtained by obtaining a plurality of samples of the microbiome from one or more subjects and associated with each sample the health or performance score or metric and by measuring in each sample the concentration of each of the plurality of metabolites,
receiving, by a computing device, the plurality of initial data sets of respective ones of the plurality of subjects,
the metabolite analyzer comprising an application, server, service, daemon, routine, or other executable logic,
wherein the metabolite analyzer includes
a classifier,
a shadow data generator, and
a filtering engine,
wherein with the metabolite analyzer, the initial data sets are received;
wherein the filtered data set is the subset of the plurality of metabolites.
Claim 15: see limitations in claim 1 above as well as the following additional element
a time-of-flight mass spectrometer, a gas chromatograph or a liquid chromatograph for measuring the concentration of each of a plurality of metabolites in a sample of a microbiome of a respective subject
a computing device comprising a processor
The following limitations:
“obtaining a plurality of initial data sets, each of the plurality of initial data sets comprising measurement data comprising an indication of a concentration of each of a plurality of metabolites in a sample of a microbiome of a respective subject, and a label at least in part characterizing the state of the subject, the label being a health or performance score or metric,
wherein the plurality of initial data sets is obtained by obtaining a plurality of samples of the microbiome from one or more subjects and associated with each sample the health or performance score or metric and by measuring in each sample the concentration of each of the plurality of metabolites,
receiving, by a computing device, the plurality of initial data sets of respective ones of the plurality of subjects;
wherein the filtered data set is the subset of the plurality of metabolites,”
are pre-solution activities (see MPEP 2106.05(g)), because they’re used to obtain additional information about a plurality of data sets.
Additionally, the following limitations,
“the metabolite analyzer comprising an application, server, service, daemon, routine, or other executable logic,
wherein the metabolite analyzer includes
a classifier,
a shadow data generator, and
a filtering engine,
wherein with the metabolite analyzer, the initial data sets are received,
a time-of-flight mass spectrometer, a gas chromatograph or a liquid chromatograph for measuring the concentration of each of a plurality of metabolites in a sample of a microbiome of a respective subject
a computing device comprising a processor,”
are merely reciting the computer components at a high-level of generality. In other words, the computer components are being used as a tool to carry out the system’s functions (See MPEP 2106.05(f)).
Thus, the abstract idea is not integrated into a practical application. The combination of these additional elements is no more than insignificant extra solution activity, and generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additionally, regarding the limitations directed to
“the metabolite analyzer comprising an application, server, service, daemon, routine, or other executable logic,
wherein the metabolite analyzer includes
a classifier,
a shadow data generator, and
a filtering engine,
a time-of-flight mass spectrometer, a gas chromatograph or a liquid chromatograph,”
see Rose et al. (US 2020/0227166), which discloses performing clinical assessments [0009] by measuring a panel of analytes [0009], and teaches
a metabolite analyzer ([0011]: analytes may comprise of metabolites; [0164]: models may be used to analyze analyte data) comprising an
application,
server,
service,
daemon,
routine, or
other executable logic [0164],
wherein the metabolite analyzer includes
a classifier ([0164]: random forest is a classifier; [0330]),
a shadow data generator
([0232]: random shuffling of data is interpreted as a shadow data generator, as also evidenced by Applicant’s spec “a second set of “shadow” training data 122 may be generated by randomizing or shuffling the training data 120 (e.g. for each sample, assigning a random measurement value to each metabolite in some implementations; or by randomly shuffling” [0056]; [0212]), and
a filtering engine [0301],
a time-of-flight mass spectrometer [0246], a gas chromatograph [0226] or a liquid chromatograph [0226].
Thus, the limitations directed to
“the metabolite analyzer comprising an application, server, service, daemon, routine, or other executable logic,
wherein the metabolite analyzer includes
a classifier,
a shadow data generator, and
a filtering engine,
a time-of-flight mass spectrometer, a gas chromatograph or a liquid chromatograph,”
are well-understood, routine, and conventional, as evidenced by the reference above.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra solution activity and generic computer components.
The same analysis applies here in 2B and does not provide an inventive concept.
Therefore, none of the claims 40-59 amount to significantly more than the abstract idea itself. Accordingly, claims 40-59 are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al. and 2019 PEG.
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.
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.
Claims 40-43, 45, and 47-57 are rejected under 35 U.S.C. 103 as being anticipated by Apte et al. (US 2018/0122510) in view of Roth et al. (US 11816185) in view of He et al. (US 2017/0082555).
In re claim 40, Apte discloses a method (fig. 1A: 100) of identifying a set of predictor metabolites (S110; [0034]: block S110 includes assessment and processing of metabolites) which are predictive of a state of a subject ([0056]: predicts characterizations of a population of subjects such as health state, health conditions, metabolic factors, etc.) being an animal subject ([0026]: one or more users may be an animal subject), comprising:
obtaining a plurality of initial data sets (S110; [0033]: S110 receives set of biological samples associated with a population of subjects; [0018]),
each of the plurality of initial data sets comprising
measurement data comprising an indication of a concentration of each of a plurality of metabolites in a sample of a microbiome of a respective subject ([0030]: biological sample is analyzed for characteristics in relation to diet-related conditions; [0065]: characterization process occurs for one or more users which identifies microbiome and concentration from blood sample test; [0088]: characterization process includes “Biosynthesis and biodegradation of secondary metabolites (KEGG3)”; [0018]: includes microbiome composition dataset), and
a label at least in part characterizing the state of the subject ([0053]: characterization process includes labels such as identifying features and/or feature combination used to characterize subjects based upon their microbiome composition including behavioral traits and medical conditions),
the label being a health or performance score ([0020]: method 100 can include analysis that provides a score) or metric ([0059]: characterization can include ranking of microbiome features which would provide a metric),
wherein the plurality of initial data sets is obtained by obtaining a plurality of samples of the microbiome from one or more subjects (fig. 5: S130 involves characterization for a set of biological samples) and associated with each sample the health or performance score or metric [0059] and by measuring in each sample the concentration of each of the plurality of metabolites ([0065]: characterization can be related to diet conditions which includes improvement of concentration),
receiving, by a computing device [0135], the plurality of initial data sets of respective ones of the plurality of subjects (S110; [0033]: S110 receives set of biological samples associated with a population of subjects; [0018]),
applying, by the computing device, a feature selection process ([0056-0057]: characterization process is performed by feature vectors which are effective in predicting classifications) to the plurality of initial data sets to select and thereby identify a subset of the plurality of metabolites of which subset the concentrations are a statistically significant predictor of the state according to the label ([0065-0066]: characterization process is used to identify predictor of diet condition and can be performed on different diet sets based on microbiome composition features; [0069]: can identify a set of microbiomes composition features and identify health-supporting measures to have an effect on the subject) in that with the computing device a metabolite analyzer is executed, the metabolite analyzer comprising an application ([0097]: characterization process includes analyzing metabolites), server [0135], service, daemon, routine, or other executable logic [0135],
wherein the metabolite analyzer includes
a classifier [0064-0065],
a filtering engine ([0059]: Block S130 may include filtering),
wherein with the metabolite analyzer, the initial data sets are received [0033],
wherein by executing the classifier, for each metabolite of the initial data sets, importance scores for the importance of the respective metabolite for contributing to a corresponding health or performance score or metric are calculated ([0020-0021]: Concordance Score is given for each microbiome according to traits and a score higher than .5 indicates a positive relationship between groups),
wherein by executing the classifier, for each metabolite of the shadow data sets, importance scores for the importance of the respective metabolite for contributing to a corresponding health or performance score or metric are calculated [0020-0021].
Apte fails to disclose
wherein the metabolite analyzer includes …a shadow data generator, and
wherein by executing the shadow data generator, shadow data sets are generated from the initial data sets by random reshuffling the measurements of the concentrations the metabolites and/or health or performance scores or metrics or by generating new random values,
wherein by executing the filtering engine, the importance scores from the initial data sets are compared to the importances scores from the shadow data sets to identify scores above a threshold and filter a subset of metabolites in that for each metabolite,
for the respective metabolite, the importance score of the initial data sets is selected and compared to the importance score for the respective metabolite of the shadow data sets,
wherein, if the importance score for the metabolite of the initial data sets exceeds the importance score of the shadow data sets, the respective metabolite is added to the filtered data set and otherwise excluded from the filtered data set,
wherein the filtered data set is the subset of the plurality of metabolites.
Roth teaches using deep learning (Col. 2, lines 11-15) to perform analysis of volumetric quantification of parameters of three-dimensional objects (Col. 2, lines 30-34), and teaches
wherein the analysis includes a shadow data generator
(Col. 17, lines 54-65: training data is shuffled by a random shuffling to generate a truly random ordering; see Applicant’s spec: “a second set of “shadow” training data 122 may be generated by randomizing or shuffling the training data 120 (e.g. for each sample, assigning a random measurement value to each metabolite in some implementations; or by randomly shuffling” [0056])
applied to initial data sets (Col. 17, lines 54-65: training data is shuffled) and
wherein by executing the shadow data generator, shadow data sets are generated from the initial data sets by random reshuffling (Col. 17, lines 54-65: training data is shuffled).
Roth further teaches that shuffling changes the arrangement in which the data is used so a training algorithm doesn’t encounter groupings of similar types of data (Col. 17, lines 54-65).
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the method of identifying a set of predictor metabolites taught by Apte, to provide wherein by executing the shadow data generator, shadow data sets are generated from the initial data sets by random reshuffling the measurements of the concentrations the metabolites and/or health or performance scores or metrics or by generating new random values, as taught by the shadow data generator of Roth which randomly reshuffles shadow data sets, because shuffling changes the arrangement in which the data is used so a training algorithm doesn’t encounter groupings of similar types of data
The proposed combination would yield wherein by executing the shadow data generator of Roth, shadow data sets are generated from the initial data sets of Apte by randomly reshuffling the measurements of the concentrations of the metabolites and/or health or performance scores or metrics of Apte.
He teaches a method for classifying defects on a specimen [0002], and teaches
an analyzer ([0055]: SVM analyzes data) comprising an application, server, service, daemon, routine, or other executable logic [0055],
wherein the analyzer includes
a classifier [0055],
a data generator ([0109]: created defect classifier is a data generator; [0012]: defect classifier is created based on received classifications), and
a filtering engine ([0124]: decision tree can be added to filter out defects with a large height; [0100]),
wherein by executing the filtering engine, importance scores from initial data sets ([0012]: confidence scores are calculated i.e. importance scores because they detect whether there are detects) are compared to importances scores from cluster of data sets ([050]: groups of the detects are generated; [0096]: clusters of detects) to identify scores above a threshold ([0109]: [0109]: final novel bin is compared with a threshold to trigger re-training of the defect classifier) and filter a subset of metabolites in that for each dataset ([0100]: clusters of new defects are filtered),
for the respective dataset [0100, 0109], the importance score of the initial data sets is selected and compared to the importance score for respective clusters of the data sets ([0109]: comparison made to size to detect if it’s greater than a threshold to trigger re-training),
wherein, if the importance score for the clusters of the initial data sets exceeds the importance score of the shadow data sets, the respective clusters is added to the filtered data set and otherwise excluded from the filtered data set ([0109]: when final novel bin is above the threshold, the defects are filtered when re-training is triggered; [0100]: clusters of new defects are filtered),
wherein the filtered data set is the subset of the plurality of the clusters ([0100]: filtered data set would be subset of all the data set and comprise defects).
He further teaches that using a threshold to retrigger training catches defects [0109] and avoids incorrectly caught defects [0109].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the method of identifying a set of predictor metabolites taught by Apte, to provide wherein by executing the filtering engine, the importance scores from the initial data sets are compared to the importances scores from the data sets to identify scores above a threshold and filter a subset of cluster data sets in that for each data set, for the respective data set, the importance score of the initial data sets is selected and compared to the importance score for the respective data set of the clusters of data sets, wherein, if the importance score for the metabolite of the initial data sets exceeds the importance score of the shadow data sets, the respective data set is added to the filtered data set and otherwise excluded from the filtered data set, wherein the filtered data set is the subset of the plurality of data sets, as taught by the analyzer of He, because using a threshold to retrigger training catches defects and avoids incorrectly caught defects.
The proposed combination would yield wherein filtering engine and the shadow data generator of the proposed combination would be modified to include a comparison being made between the initial data sets of the metabolites and the shadow data sets (as taught by a comparison of the data sets of He) and would yield wherein, if the importance score for the metabolite of the initial data sets exceeds the importance score of the shadow data sets, the respective metabolite is added to the filtered data set and otherwise excluded from the filtered data set.
In re claim 41, the proposed combination yields (all mapping directed to Apte unless otherwise stated) wherein the measurement data is obtained by:
subjecting a test group of animal subjects to a stimulus to affect a state of the animal subjects, or providing a test group of animal subjects subjected to the stimulus ([0055]: first group of subjects exhibiting a target state i.e. a diet-related state which is being affected by stimulus; [0019]: diet-related state includes different diets), and
providing a control group of animal subjects which are not subjected to the stimulus ([0055]: second group of subjects not exhibiting a target state); and
wherein the label is indicative of whether a respective animal subject is part of the test group or the control group of animal subjects ([0055]: characterization is based upon features derived from a statistical analysis of similarities/differences between a control group and a test group, which would indicate which group the animal subject is part of).
In re claim 42, the proposed combination yields (all mapping directed to Apte unless otherwise stated) wherein subjecting the test group of animal subjects to the stimulus comprises at least one of:
supplying a nutritional additive to feed ([0019]: promotes characterizations of diet-related conditions and includes supplying nutritional additive to feed such as from raw meat diets; [0055]: test group exhibits a diet-related state) and/or drinking water of the test group of animal subjects (optional);
topically administering a composition comprising a skin-care active to the skin of the test group of animal subjects;
subjecting the test group of animal subjects to a pathogen;
controlling an environmental parameter of an environment of the test group of animal subjects;
controlling a size and/or type of space in which the test group of animal subjects are kept;
controlling a density of animal subjects in the test group of animal subjects; and
controlling access of the test group of animal subjects to an outside environment.
In re claim 43, the proposed combination yields (all mapping directed to Apte unless otherwise stated) wherein the label characterizes
a health state ([0053]: identified features can include health condition states and medical conditions),
welfare state or performance state of the subject,
a growth rate,
a body weight gain,
a water consumption,
a feed consumption ([0047]: behavioral information includes sugar consumption which is feed consumption; [0024]: labels include behavioral classifications),
a feed conversion ratio,
a lean muscle mass,
a weaning weight,
a weaning age,
an egg production rate,
a fertility,
a mortality,
an infection by a pathogen,
a muscular endurance,
a methane emission rate,
a resting heart rate,
a pulmonary arterial pressure,
a stress level,
a presence or degree of repetitive behavior,
a presence or degree of aggressive behavior,
hair shedding,
feet health of cattle,
marbling of meat,
skin age,
skin moisturization,
skin sebum,
skin barrier (TEWL),
skin elasticity,
skin oiliness,
skin appearance and/or
skin glow,
of the subject.
In re claim 45, the proposed combination yields (all mapping directed to Apte unless otherwise stated) a method of identifying a metabolic mechanism or mode of action of a stimulus affecting a state of an animal subject (see in re claim 40 above and also [0065]: identifies metabolic factors i.e. metabolic mechanism based on dietary condition), the method comprising:
receiving an identification of a set of predictor metabolites which are predictive of the state of the animal subject ([0034]: assessment is based on markers which includes metabolites; ([0079]: metabolites may be identified for performing the characterization process), wherein the set of predictor metabolites are identified by the method of claim 41 (see above) using a test group of animal subjects subjected to a stimulus [0055];
identifying one or more metabolic pathways associated with the set of the plurality of metabolites ([0071]: can detect Biosynthesis and biodegradation of secondary metabolites functions (KEGG3) which is a metabolic pathway);
based on said identified one or more pathways, identifying a metabolic mechanism or mode of action of the stimulus ([0071]: based on the metabolic pathway, can identify diet set and perform characterization process).
In re claim 47, regarding the limitation, “a method of determining whether an animal subject has been or is being subjected to a stimulus, comprising - identifying a set of predictor metabolites by the method of claim 40, wherein the set of predictor metabolites comprises at least one predictor metabolite selected from N- acetylphenylalanine; phenyllactate (PLA); N-acetylvaline; linolenate (18:3n3 or 3n6); N- acetylleucine; N-butyryl-leucine; N-acetylisoleucine; pterin; 1-palmitoyl-2-linoleoyl- galactosylglycerol (16:0/18:2); and methylphosphate,” see in re claim 44 above.
In re claim 48, regarding the limitation, “a method of treating an animal subject by supplying a nutritional additive as determined by the method of claim 46 to feed and/or drinking water of an animal subject”, see in re claim 42 above.
In re claim 49, the proposed combination yields (all mapping directed to Apte unless otherwise stated) a method of predicting a current or future state of an animal subject (see in re claim 40 above), comprising:
receiving, by a computing device (see in re claim 40 above), an identification of a set of predictor metabolites ([0079]: metabolites may be identified for performing the characterization process) which are predictive of a state of an animal subject [0065],
wherein the set of predictor metabolites are identified by the method of claim 40 (see above);
receiving, by the computing device, measurement data comprising an identification of concentrations of metabolites in a sample of a microbiome of the animal subject ([0069]: identifies a set of microbiomes composition features; [0061]: microbiome features may be used as inputs i.e. measurement data);
filtering, by the computing device, the measurement data for concentrations of the set of predictor metabolites in the sample ([0059]: filtering may be added to the data processing, such as to the concentration of the metabolites); and
predicting, by the computing device, the current or future state of the animal subject based on the concentrations of the set of predictor metabolites ([0065]: current dietary condition may be predicted based on concentration).
In re claim 50, the proposed combination yields (all mapping directed to Apte unless otherwise stated) a method of identifying a presence of a pathogen affecting a state of an animal subject ([0069]: identifies microbiome composition features which may affect health of a subject; [0033-0034]: analyzes markers derived from bacteria; [0026, 0029]), the method comprising:
receiving, by a computing device, an identification of a set of predictor metabolites which are predictive of the state of the animal subject (see in re claim 49 above),
wherein the set of predictor metabolites are identified by the method of claim 41 using a test group of animal subjects subjected to a pathogen ([0055]: test group i.e. first group of subjects may be used for characterization; [0066]: characterization process determines microbiome features such as different bacteria which are associated with pathogens; [0047]: behavioral information includes disease states);
receiving, by the computing device, measurement data comprising an identification of concentrations of metabolites in a sample of a microbiome of the animal subject (see in re claim 49 above);
filtering, by the computing device, the measurement data for concentrations of the set of predictor metabolites in the sample (see in re claim 49 above); and
predicting, by the computing device, a presence of the pathogen in the animal subject based on the concentrations of the set of predictor metabolites ([0033]: output may be presence of bacteria i.e. a pathogen).
In re claim 51, the proposed combination yields (all mapping directed to Apte unless otherwise stated) a method of monitoring a state of an animal subject (see in re claim 40 above), comprising:
receiving, by a computing device, an identification of a set of predictor metabolites which are predictive of a state of an animal subject (see in re claim 54 above),
wherein the set of predictor metabolites are identified by the method of claim 40 (see above);
receiving, by the computing device, measurement data comprising an identification of concentrations of metabolites in a sample of a microbiome of the animal subject (see in re claim 49 above);
filtering, by the computing device, the measurement data for concentrations of the set of predictor metabolites in the sample (see in re claim 49 above); and
providing, by the computing device, an output signal which is indicative of one or more of the concentrations of respective predictor metabolites corresponding to or deviating from one or more reference concentration for the respective predictor metabolites ([0105]: therapy model may be derived in relation to a baseline microbiome composition i.e. a reference concentration and may output therapy regimes to shift the microbiomes of subjects towards the reference concentrations).
In re claim 52, the proposed combination yields (all mapping directed to Apte unless otherwise stated) a non-transitory computer readable medium [0134-0135] comprising one or more instructions, the execution of which cause a processor [0145] of a computing device [0135] to perform the method of claim 40 [0134-0135].
In re claim 53, Apte discloses a system [0134-0135] for identifying a set of predictor metabolites which are predictive of a state of a subject being a mammal subject (see in re claim 40 above), comprising:
a time-of-flight mass spectrometer, a gas chromatograph or a liquid chromatograph ([0037]: chromatography can be done with a column absorption and would be liquid) for measuring the concentration of each of a plurality of metabolites in a sample of a microbiome of a respective subject ([0065]: concentration of samples can be detected for improvement; [0037]) and
a computing device [0135] comprising a processor [0145].
Regarding the limitations,
“…a computing device comprising a processor configured to:
receive a plurality of initial data sets of respective ones of a plurality of subjects,
wherein each of the plurality of initial data sets comprises:
measurement data comprising an indication of the concentration of each of the plurality of metabolites in the sample of a microbiome of the respective subject, and
a label at least in part characterizing the state of the subjective label being a health or performance score or metric;
apply a feature selection process to the plurality of initial data sets to select and thereby identify a subset of the plurality of metabolites of which subset the concentrations are a statistically significant predictor of the state according to the label in that with the computing device a metabolite analyzer is executed,
the metabolite analyzer comprising an application, server, service, daemon, routine, or other executable logic,
wherein the metabolite analyzer includes
a classifier,
a filtering engine,
wherein with the metabolite analyzer, the initial data sets are received,
wherein by executing the classifier, for each metabolite of the initial data sets, importance scores for the importance of the respective metabolite for contributing to a corresponding health or performance score or metric are calculated,
wherein by executing the classifier, for each metabolite of the shadow data sets, importance scores for the importance of the respective metabolite for contributing to a corresponding health or performance score or metric are calculated,
see in re claim 40 above.
Regarding the limitations,
wherein the metabolite analyzer includes …a shadow data generator, and
wherein by executing the shadow data generator, shadow data sets are generated from the initial data sets by random reshuffling the measurements of the concentrations the metabolites and/or health or performance scores or metrics or by generating new random values,
wherein by executing the filtering engine, the importance scores from the initial data sets are compared to the importances scores from the shadow data sets to identify scores above a threshold and filter a subset of metabolites in that for each metabolite,
for the respective metabolite, the importance score of the initial data sets is selected and compared to the importance score for the respective metabolite of the shadow data sets,
wherein, if the importance score for the metabolite of the initial data sets exceeds the importance score of the shadow data sets, the respective metabolite is added to the filtered data set and otherwise excluded from the filtered data set,
wherein the filtered data set is the subset of the plurality of metabolites”,
see in re claim 40 above.
In re claim 54, the proposed combination yields (all mapping directed to Apte unless otherwise stated) a system for identifying a metabolic mechanism or mode of action of a stimulus affecting a state of an animal subject (see in re claim 40 above, wherein the method identifies a diet of an animal), comprising: a computing device comprising a processor (see in re claim 53 above) configured to:
receive an identification of a set of predictor metabolites which are predictive of the state of the animal subject (see in re claim 49 above),
wherein the set of predictor metabolites are identified by the method of claim 41 using a test group of animal subjects subjected to a stimulus (see in re claim 41 above);
identify one or more metabolic pathways associated with the subset of the plurality of metabolites (see in re claim 45 above);
based on said identified one or more pathways, identify a metabolic mechanism or mode of action of the stimulus (see in re claim 45 above).
In re claim 55, the proposed combination yields (all mapping directed to Apte unless otherwise stated) a system for predicting a current [0065] or future state of an animal subject [0065], comprising: a computing device comprising a processor (see in re claim 53 above) configured to:
receive an identification of a set of predictor metabolites which are predictive of a state of an animal subject (see in re claim 54 above), wherein the set of predictor metabolites are identified by the method of claim 40 (see above);
receive measurement data comprising an identification of concentrations of metabolites in a sample of a microbiome of the animal subject (see in re claim 49 above);
filter the measurement data for concentrations of the set of predictor metabolites in the sample; and predict the current or future state of the animal subject based on the concentrations of the set of predictor metabolites (see in re claim 49 above).
In re claim 56, the proposed combination yields (all mapping directed to Apte unless otherwise stated) a system for identifying a presence of a pathogen affecting a state of an animal subject ([0069]: identifies microbiome composition features which may affect health of a subject; [0026, 0029]; see also claim 50 above), comprising: a computing device comprising a processor (see in re claim 53 above) configured to:
receive an identification of a set of predictor metabolites which are predictive of the state of the animal subject (see in re claim 49 above),
wherein the set of predictor metabolites are identified by the method of claim 41 using a test group of animal subjects subjected to a pathogen (see in re claim 50 above);
receive measurement data comprising an identification of concentrations of metabolites in a sample of a microbiome of the animal subject (see in re claim 49 above);
filter the measurement data for concentrations of the set of predictor metabolites in the sample (see in re claim 49 above); and
predict a presence of the pathogen in the animal subject based on the concentrations of the set of predictor metabolites (see in re claim 50 above).
In re claim 57, the proposed combination yields (all mapping directed to Apte unless otherwise stated) a system for monitoring a state of an animal subject (see in re claim 53 above), comprising: a computing device comprising a processor (see in re claim 53 above) configured to:
receive an identification of a set of predictor metabolites which are predictive of a state of an animal subject (see in re claim 54 above), wherein the set of predictor metabolites are identified by the method of claim 40 (see above);
receive measurement data comprising an identification of concentrations of metabolites in a sample of a microbiome of the animal subject (see in re claim 49 above);
filter the measurement data for concentrations of the set of predictor metabolites in the sample (see in re claim 49 above); and
provide an output signal which is indicative of one or more of the concentrations of respective predictor metabolites corresponding to or deviating from one or more reference concentration for the respective predictor metabolites ([0105]: therapy model may be derived in relation to a baseline microbiome composition i.e. a reference concentration and may output therapy regimes to shift the microbiomes of subjects towards the reference concentrations).
Claims 44, 46, and 5-59 are rejected under 35 U.S.C. 103 as being unpatentable over Apte et al. (US 2018/0122510) in view of Roth et al. (US 11816185) in view of He et al. (US 2017/0082555) in view of Segal et al. (US 2022/0102000).
In re claim 44, Apte fails to disclose a method of determining whether an animal subject has been or is being subjected to a stimulus, comprising identifying a set of predictor metabolites by the method of claim 40, wherein the set of predictor metabolites comprises at least one predictor metabolite selected from
N- acetylphenylalanine;
phenyllactate (PLA);
N-acetylvaline;
linolenate (18:3n3 or 3n6);
N- acetylleucine;
N-butyryl-leucine;
N-acetylisoleucine; pterin; 1-palmitoyl-2-linoleoyl- galactosylglycerol (16:0/18:2); and
methylphosphate.
Segal teaches a non-invasive method [0003] of quantifying blood metabolites [0003], and teaches analyzing multiple metabolites [0074], including phenyllactate (PLA) ([0074] (pg. 8, left column, line 47)] and N- acetylleucine ([0074] (pg. 8, left column, line 8)] and various other metabolites [0074].
Segal further teaches that over 1000 metabolites may be analyzed [0063] to better understand alterations in metabolites under different conditions [0063] so that interventions for manipulating metabolite levels may be designed [0063].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify a method of determining whether an animal subject has been or is being subjected to a stimulus taught by Apte, to provide wherein the set of predictor metabolites comprises at least one of phenyllactate (PLA) and N - acetylleucine, as taught by Segal, because over 1000 metabolites may be analyzed to better understand alterations in metabolites under different conditions so that interventions for manipulating metabolite levels may be designed.
In re claim 46, regarding the limitations, “further comprising, based on said identified metabolic mechanism or mode of action of the stimulus, determining a type and/or a concentration of one or more nutritional additives which, when ingested by the animal subject, generate the effect of the stimulus on the state of the animal subject, preferably wherein the set of predictor metabolites comprises N-acetylphenylalanine; phenyllactate (PLA); N-acetylvaline; linolenate (18:3n3 or 3n6); N-acetylleucine; N-butyryl-leucine; N-acetylisoleucine; pterin; 1-palmitoyl-2-linoleoyl- galactosylglycerol (16:0/18:2); and/or methylphosphate”, see in re claim 44 above.
In re claim 58, regarding the limitations, “wherein the set of predictor metabolites comprises at least two, three, four, five, six, seven, eight, nine, or even ten predictor metabolites selected from N-acetylphenylalanine; phenyllactate (PLA); N-acetylvaline; linolenate (18:3n3 or 3n6); N-acetylleucine; N-butyryl-leucine; N-acetylisoleucine; pterin; 1-palmitoyl-2-linoleoyl- galactosylglycerol (16:0/18:2); and methylphosphate”, see in re claim 44 above.
In re claim 59, regarding the limitations, “wherein the set of predictor metabolites comprises at least two, three, four, five, six, seven, eight, nine, or even ten predictor metabolites selected from N-acetylphenylalanine; phenyllactate (PLA); N-acetylvaline; linolenate (18:3n3 or 3n6); N-acetylleucine; N-butyryl-leucine; N-acetylisoleucine; pterin; 1-palmitoyl-2-linoleoyl- galactosylglycerol (16:0/18:2); and methylphosphate”, see in re claim 44 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUMAISA R BAIG whose telephone number is (571)270-0175. The examiner can normally be reached Mon-Fri: 8am- 5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at (571) 270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RUMAISA RASHID BAIG/Examiner, Art Unit 3796
/DAVID HAMAOUI/SPE, Art Unit 3796
Prosecution Insights