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 .
Status of Claims
This action is a non-final rejection
Claims 1-22, 24-25, 38-39, 45, 47, 70-71, 74-77, 82-83, 86, 89, 98-99 are pending
Claims 70-71, 74-77, 82-83, 86, 89 were withdrawn after restriction
Claims 1-22, 24-25, 38-39, 45, 47, 98-99 were elected
Claims 1-22, 24-25, 38-39, 45, 47, 98-99 are rejected under 35 USC § 101
Claims 22, 45 are rejected under 35 USC § 112
Claims 1-4, 7, 15-18, 21-22, 24-25, 38, 45, 47, 98 are rejected under 35 USC § 102
Claims 5-6, 8, 9-14, 19-20, 39, 99 are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a domestic priority date of 10-2-2020
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 4-18-2023, 3-31-2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 22 and 45 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The terms in the claims starting with “denovo” followed by 3 or 4 digits in claims 22 and 45 are used by the claim to mean as interpreted by the Examiner to refer to the 3 or 4 digits as listed under the column labelled under “OUT_ID” of figures 7, 8, 9 while the accepted meaning is listed under the column labelled under “Taxonomy” of figures 7, 8, 9.The terms are indefinite because the specification does not clearly redefine the terms.
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 1-22, 24-25, 38-39, 45, 47, 98-99 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-22, 24-25, 38-39, 45, 47, 98-99 the claims recite an abstract idea of “animal diagnostics for determining an animal’s age”.
Independent Claims 1, 24 and 98 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 1, 24 recite a device, and method respectively regarding “animal diagnostics for determining an animal’s age”.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
store health information associated with a plurality of animals;
obtain input data for an animal, wherein:
the animal is a member of the canid family;
the input data comprises a first array comprising a first plurality of entries; and
each entry within the first plurality of entries comprises a numerical value that indicates an amount of a type of bacteria that is present within a sample from the animal;
input the input data for the animal into a .. model, wherein the .. model is configured to:
receive the input data for the animal; and
output an animal age value based at least in part on the input data for the animal, wherein the animal age value identifies a predicted age for the animal;
obtain the animal age value from the .. model;
and output the animal age value.
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “animal diagnostics for determining an animal’s age”. Alternatively it belongs to certain methods of organizing nonhuman animal activity under managing personal behavior or relationships or interactions between animals as it recites “animal diagnostics for determining an animal’s age”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 1 (Does the claim fall within a statutory category? YES): claim 98 recites a machine learning model training method regarding “animal diagnostics for determining an animal’s age”.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
obtaining ..data for a plurality of animals, wherein:
the .. data indicates an amount of a type of bacteria that is present within a sample for each animal from among the plurality of animals; and
the plurality of animals are members of the canid family;
associating the .. data with animal age values, wherein associating the ..data with the animal age values comprises associating each animal from among the second plurality of animals with an animal age value; and
training a .. model using the .. data that is associated with the animal age values, wherein the .. model is configured to:
receive input data for an animal; and
output an animal age value based at least in part on the input data for the animal, wherein the animal age value identifies a predicted age for the animal.
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “animal diagnostics for determining an animal’s age”. Alternatively it belongs to certain methods of organizing nonhuman animal activity under managing personal behavior or relationships or interactions between animals as it recites “animal diagnostics for determining an animal’s age”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claims 1, 24 and 98 recite:
machine learning model.
Claim 1 recites:
a memory;
a processor operably coupled to the memory;
Claim 98 recites:
Training data;
Amount to no more than mere instructions to apply the exception using a generic computer, or merely using a computer as a tool to implement the abstract idea as even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claims 1, 24 and 98 recite:
machine learning model.
Claim 1 recites:
a memory;
a processor operably coupled to the memory;
Claim 98 recites:
Training data;
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, the additional elements alone, and in combination do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims recite additional limitations that further define the abstract idea of “animal diagnostics for determining an animal’s age”. These claim limitations include:
Claim 2:
the input data for the animal further comprises an animal size classification value; and
the model is further configured to output the animal age value based at least in part on the animal size classification.
Claim 3:
the input data for the animal further comprises an animal breed identifier that identifies a breed of the animal; and
the model is further configured to output the animal age value based at least in part on the animal breed identifier.
Claim 4:
the input data for the animal further comprises a weight value that identifies a weight for the animal; and
the model is further configured to output the animal age value based at least in part on the weight value;
Claim 5: the acts further including:
the input data for the animal further comprises a gingivitis value for the animal;
the gingivitis value is associated with a time to bleeding when probing a mouth of the animal; and
the model is further configured to output the animal age value based at least in part on the gingivitis value.
Claim 6:
the input data for the animal further comprises a periodontitis value for the animal;
the periodontitis value is associated with an amount of periodontitis that is present in a mouth of the animal; and
the model is further configured to output the animal age value based at least in part on the periodontitis value.
Claim 7:
the input data for the animal further comprises geographic location information for a physical location associated with the animal; and
the model is further configured to output the animal age value based at least in part on the geographical information.
Claim 8: wherein the sample is collected while the animal is conscious.
Claim 9: wherein the sample comprises bacteria from a gingival area in a mouth of the animal.
Claim 10: wherein the sample comprises bacteria from a supragingival area in a mouth of the animal.
Claim 11: wherein the sample is collected while the animal is unconscious.
Claim 12: wherein the sample comprises bacteria from a gingival area in a mouth of the animal.
Claim 13: wherein the sample comprises bacteria from a subgingival area in a mouth of the animal.
Claim 14: wherein the sample comprises bacteria from a supragingival area in a mouth of the animal.
Claim 15, 38:
obtain data for a second plurality of animals, wherein the data indicates an amount of a type of bacteria that is present within a sample for each animal from among the second plurality of animals;
associate the data with animal age values, wherein associating the data with the animal age values comprises associating each animal from among the second plurality of animals with an animal age value; and
train the model using the data that is associated with the animal age values.
Claim 16: associate the data with animal size classification values before training the model, wherein associating the data with the animal size classification values comprises associating each animal from among the second plurality of animals with an animal size classification value.
Claim 17: associate the data with animal breed identifiers before training the model, wherein associating the data with the animal breed identifiers comprises associating each animal from among the second plurality of animals with an animal breed identifier.
Claim 18: associate the data with weight values before training the model, wherein associating the data with the weight values comprises associating each animal from among the second plurality of animals with a weight value
Claim 19: associate the data with gingivitis values before training the model, wherein associating the data with the gingivitis values comprises associating each animal from among the second plurality of animals with a gingivitis value
Claim 20: associate the data with periodontitis values before training the model, wherein associating the data with the periodontitis values comprises associating each animal from among the second plurality of animals with a periodontitis value.
Claim 21: associate the data with geographic location information before training the model, wherein associating the data with the geographic location information comprises associating each animal from among the second plurality of animals with a physical location.
Claim 22, 45: wherein the sample comprises:
a) one or more bacteria selected from a group comprising denovo483, denovo7761, denovol3434, denovol1506, denovo6559, denovol1018, denovo11779, denovo5898, denovo7616, and denovo4478;
and/or b) one or more bacteria selected from a group comprising denovo483, denovo7761, denovo5898, denovol3434, denovo248, denovo11018, denovo2415, denovol1506, denovo264, and denovo715.
Claim 25:
a ) the input data for the animal further comprises
i)an animal size classification value
ii) an animal breed identifier that identifies a breed of the animal,
iii) a weight value that identifies a weight for the animal,
iv) a gingivitis value for the animal, wherein the gingivitis value is associated with a time to bleeding when probing a mouth of the animal,
v) a periodontitis value for the animal, wherein the periodontitis value is associated with an amount of periodontitis that is present in a mouth of the animal, and/or
vi) geographic location information for a physical location associated with the animal; and
b) the machine learning model is further configured to
i) output the animal age value based at least in part on the animal size classification
ii) output the animal age value based at least in part on the animal breed identifier,
iii) output the animal age value based at least in part on the weight value,
iv) output the animal age value based at least in part on the gingivitis value,
v) output the animal age value based at least in part on the periodontitis value, and/or
vi) output the animal age value based at least in part on the geographical information.
Claim 39:
a) associating the data with animal size classification values before training the model, wherein associating the data with the animal size classification values comprises associating each animal from among the second plurality of animals with an animal size classification value;
b) associating the data with animal breed identifiers before training the model, wherein associating the data with the animal breed identifiers comprises associating each animal from among the second plurality of animals with an animal breed identifier
c) associating the data with weight values before training the model, wherein associating the data with the weight values comprises associating each animal from among the second plurality of animals with a weight value;
d) associating the data with gingivitis values before training the model, wherein associating the data with the gingivitis values comprises associating each animal from among the second plurality of animals with a gingivitis value;
e) associating the data with periodontitis values before training the model, wherein associating the data with the periodontitis values comprises associating each animal from among the second plurality of animals with a periodontitis value;
f) associating the data with geographic location information before training the model, wherein associating the data with the geographic location information comprises associating each animal from among the second plurality of animals with a physical location.
Claim 99: further comprising:
a) associating the data with animal size classification values before training the model, wherein associating the data with the animal size classification values comprises associating each animal from among the plurality of animals with an animal size classification value:
b) associating the data with animal breed identifiers before training the model, wherein associating the data with the animal breed identifiers comprises associating each animal from among the plurality of animals with an animal breed identifier;
c) associating the data with weight values before training the model, wherein associating the data with the weight values comprises associating each animal from among the plurality of animals with a weight value;
d) associating the data with gingivitis values before training the model, wherein associating the data with the gingivitis values comprises associating each animal from among the plurality of animals with a gingivitis value;
e) associating the data with periodontitis values before training the model, wherein associating the data with the periodontitis values comprises associating each animal from among the plurality of animals with a periodontitis value;
and/or f) associating the data with geographic location information before training the model, wherein associating the data with the geographic location information comprises associating each animal from among the plurality of animals with a physical location.
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims amount to no more than mere instructions to apply the exception using a generic computer, or merely using a computer as a tool to implement the abstract idea as even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claim 2:
machine learning model
Claim 3:
machine learning model.
Claim 4:
machine learning model;
Claim 5: the acts further including:
machine learning model.
Claim 6:
machine learning model.
Claim 7:
machine learning.
Claim 15, 38:
training data
train the machine learning model.
Claim 16:
training data
training the machine learning model,
Claim 17:
training data
training the machine learning model,
Claim 18:
training data
training the machine learning model,
Claim 19:
training data
training the machine learning model,
Claim 20:
training data
training the machine learning model.
Claim 21:
training data
training the machine learning model.
Claim 39:
training data
training the machine learning model,
Claim 99: further comprising:
training data
training the machine learning model,
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the additional elements alone, and in combination do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 2:
machine learning model
Claim 3:
machine learning model.
Claim 4:
machine learning model;
Claim 5: the acts further including:
machine learning model.
Claim 6:
machine learning model.
Claim 7:
machine learning.
Claim 15, 38:
training data
train the machine learning model.
Claim 16:
training data
training the machine learning model,
Claim 17:
training data
training the machine learning model,
Claim 18:
training data
training the machine learning model,
Claim 19:
training data
training the machine learning model,
Claim 20:
training data
training the machine learning model.
Claim 21:
training data
training the machine learning model.
Claim 39:
training data
training the machine learning model,
Claim 99: further comprising:
training data
training the machine learning model;
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-4, 7, 15-18, 21-22, 24-25, 38, 45, 47, 98 are rejected by 35 U.S.C. 102(a)(1) as being anticipated by Aliper et.al. (US 20200075127 A1) hereinafter “Aliper”
Regarding claims 1 and 24 Aliper teaches:
A device, comprising: (See at least [0018] via”...processing the taxonomic profile of the microbiota of each reference subject with a computer configured with a machine learning platform in order to predict the biological age of each reference subject;...”)
a memory operable to store health information associated with a plurality of animals; (See at least [0018] via”...processing the taxonomic profile of the microbiota of each reference subject with a computer configured with a machine learning platform in order to predict the biological age of each reference subject; and saving the predicted biological age and associated taxonomic profile for each reference subject in a reference database with the predicted biological age associated with the taxonomic profile of the microbiota for each reference subject. In some aspects, the method includes: generating a computer program product stored on a tangible, non-transitory memory device of a computer that when executed cause the computer to: access the reference database; compare the subject's taxonomic profile with the reference database; provide information on at least one specific microorganism that modulates the predicted biological age associated of the subject; generate the report with the provided information; and cause the report to be provided to the subject...”)
and a processor operably coupled to the memory, configured to; (See at least [0018] via”...processing the taxonomic profile of the microbiota of each reference subject with a computer configured with a machine learning platform in order to predict the biological age of each reference subject and saving the predicted biological age and associated taxonomic profile for each reference subject in a reference database with the predicted biological age associated with the taxonomic profile of the microbiota for each reference subject. In some aspects, the method includes: generating a computer program product stored on a tangible, non-transitory memory device of a computer that when executed cause the computer to: access the reference database; compare the subject's taxonomic profile with the reference database; provide information on at least one specific microorganism that modulates the predicted biological age associated of the subject; generate the report with the provided information; and cause the report to be provided to the subject;...”)
obtain input data for an animal, wherein: (See at least [0063] via”...the subject is often a human, other mammals, such as farm animals.. dogs, cats, as well as other types of animals, such as birds, reptiles, insects etc., can also be used as subjects”; in addition see at least [0214] via”... creating input vectors based on the microbe abundance signature..”; in addition see at least [0228] via”... A method 1400 of constructing a microbiomic aging clock is provided, as described by the following steps and shown in FIG. 14. The method includes obtaining nucleic acid information contained within the guts of a cohort of subjects (block 1402). This can be done by obtaining the nucleic acids from a biological sample of each subject and performing the assays to get the information, or the information may be obtained elsewhere and the nucleic acid information is provided for this method...” )
the animal is a member of the canid family; (See at least [0063] via”...the subject is often a human, other mammals, such as ..dogs..”)
the input data comprises a first array comprising a first plurality of entries; and each entry within the first plurality of entries comprises a numerical value that indicates an amount of a type of bacteria that is present within a sample from the animal; (See at least [0214] via”... creating input vectors based on the microbe abundance signature..”; in addition see at least [0228] via”... A method 1400 of constructing a microbiomic aging clock is provided, as described by the following steps and shown in FIG. 14. The method includes obtaining nucleic acid information contained within the guts of a cohort of subjects (block 1402). This can be done by obtaining the nucleic acids from a biological sample of each subject and performing the assays to get the information...”; in addition see at least [0230] via”... The method 1400 can include inferring microbe abundance profiles from nucleic acid information (block 1406)...; )
input the input data for the animal into a machine learning model, wherein the machine learning model is configured to: (See at least [0019] via: “...inputting the input vectors into the machine learning platform..”)
receive the input data for the animal; (See at least [0063] via”... the subject is often a human, other mammals, such as farm animals.. dogs, cats, as well as other types of animals, such as birds, reptiles, insects etc., can also be used as subjects”; in addition see at least [0214] via”... creating input vectors based on the microbe abundance signature..”; in addition see at least [0228] via”...A method 1400 of constructing a microbiomic aging clock is provided, as described by the following steps and shown in FIG. 14. The method includes obtaining nucleic acid information contained within the guts of a cohort of subjects (block 1402). This can be done by obtaining the nucleic acids from a biological sample of each subject and performing the assays to get the information,...”) and
output an animal age value based at least in part on the input data for the animal, wherein the animal age value identifies a predicted age for the animal; (See at least [0019] via”...generating a predicted biological aging clock of the microbiota based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the microbiota; and preparing the report to include the biological aging clock and identifies the predicted biological age of the subject based on the microbiota..”; in addition see at least [0068] via”... creating a metagenomics biological clock and the use of it to predict a subject's biological age...”)
obtain the animal age value from the machine learning model; (See at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
and output the animal age value. (See at least [0068] via”... The model outputs the predicted phenotypic age based on the taxonomic profile...)
Regarding claim 2 Aliper teaches the invention as claimed and detailed above with respect to claim 1. Aliper also teaches:
the input data for the animal further comprises an animal size (current body mass index for subject) classification value; and the machine learning model is further configured to output the animal age value based at least in part on the animal size classification. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... current body mass index for subject.... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others...”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
Regarding claim 3 Aliper teaches the invention as claimed and detailed above with respect to claim 1. Aliper also teaches:
the input data for the animal further comprises an animal breed identifier (biometrics) that identifies a breed of the animal; and the machine learning model is further configured to output the animal age value based at least in part on the animal breed identifier. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include biometrics of subject ...The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
Regarding claim 4 Aliper teaches the invention as claimed and detailed above with respect to claim 1. Aliper also teaches:
the input data for the animal further comprises a weight (weight) value that identifies a weight for the animal; and the machine learning model is further configured to output the animal age value based at least in part on the weight value. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ..., current weight and/or weight history for subject,... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages..”)
Regarding claim 7 Aliper teaches the invention as claimed and detailed above with respect to claim 1. Aliper also teaches:
the input data for the animal further comprises geographic location information for a physical location (geographical location .. for subject) associated with the animal; and the machine learning model is further configured to output the animal age value based at least in part on the geographical information. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... geographical location .. for subject and/or family members, ... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
Regarding claims 15 and 38 Aliper teaches the invention as claimed and detailed above with respect to claims 1 and 24 respectively. Aliper also teaches:
obtain training data for a second plurality of animals, wherein the training data indicates an amount of a type of bacteria that is present within a sample for each animal from among the second plurality of animals; (See at least [0021] via”... obtaining microbiota nucleic acid information for a plurality of subjects; determining abundance profiles of the abundance of microbes of the microbiota based on the nucleic acid information for each subject; training a plurality of neural network models with the abundance profiles; assessing performance of the plurality of trained neural network models; identifying trained neural network models having an error below an error threshold; combining the identified trained neural network models into an ensemble model; and providing the ensemble model. In some aspects, the method can include: filtering the microbiota nucleic acid information prior to determining abundance profiles; filtering and normalizing the abundance profiles; and defining cross-validation sets of data, wherein the training uses the cross-validation sets of data with the filtered and normalized abundance profiles...”)
associate the training data with animal age values, wherein associating the training data with the animal age values comprises associating each animal from among the second plurality of animals with an animal age value; and train the machine learning model using the training data that is associated with the animal age values. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... current body mass index for subject.... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others...”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
Regarding claim 16 Aliper teaches the invention as claimed and detailed above with respect to claims 1 and 15. Aliper also teaches:
associate the training data with animal size classification values before training the machine learning model, wherein associating the training data with the animal size classification values comprises associating each animal from among the second plurality of animals with an animal size classification value. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... current body mass index for subject.... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others...”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
Regarding claim 17 Aliper teaches the invention as claimed and detailed above with respect to claims 1 and 15. Aliper also teaches:
associate the training data with animal breed identifiers before training the machine learning model, wherein associating the training data with the animal breed identifiers comprises associating each animal from among the second plurality of animals with an animal breed identifier. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include biometrics of subject ...The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
Regarding claim 18 Aliper teaches the invention as claimed and detailed above with respect to claims 1 and 15. Aliper also teaches:
associate the training data with weight values before training the machine learning model, wherein associating the training data with the weight values comprises associating each animal from among the second plurality of animals with a weight value. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ..., current weight and/or weight history for subject,... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
Regarding claim 21 Aliper teaches the invention as claimed and detailed above with respect to claims 1 and 15. Aliper also teaches:
associate the training data with geographic location information before training the machine learning model, wherein associating the training data with the geographic location information comprises associating each animal from among the second plurality of animals with a physical location. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... geographical location .. for subject and/or family members, ... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
Regarding claims 22 and 45 Aliper teaches the invention as claimed and detailed above with respect to claims 1 and 24 respectively. Aliper also teaches:
a) one or more bacteria selected from a group comprising denovo483, denovo7761, denovol3434, denovol1506, denovo6559, denovol1018, denovo11779, denovo5898, denovo7616, and denovo4478; and/or b) one or more bacteria selected from a group comprising denovo483, denovo7761, denovo5898, denovol3434, denovo248, denovo11018, denovo2415, denovol1506, denovo264, and denovo715 (See at least [0069] via”... In some embodiments, the different microorganisms can include two or more of the following microorganisms, where each microorganism is defined herein by the microorganism number provided in parentheses: Fusobacterium ulcerans (2),...”) Examiner interprets denovo13434 to correspond to OTU_ID 13434 Fusobacteria as seen in Figure 9 of the Drawings.
Regarding claim 25 Aliper teaches the invention as claimed and detailed above with respect to claim 24. Aliper also teaches:
a ) the input data for the animal further comprises
i)an animal size classification value (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... current body mass index for subject.... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others...”)
ii) an animal breed identifier that identifies a breed of the animal,
iii) a weight value that identifies a weight for the animal,
iv) a gingivitis value for the animal, wherein the gingivitis value is associated with a time to bleeding when probing a mouth of the animal,
v) a periodontitis value for the animal, wherein the periodontitis value is associated with an amount of periodontitis that is present in a mouth of the animal, and/or
vi) geographic location information for a physical location associated with the animal; and
b) the machine learning model is further configured to
i) output the animal age value based at least in part on the animal size classification (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... current body mass index for subject.... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others...”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
ii) output the animal age value based at least in part on the animal breed identifier,
iii) output the animal age value based at least in part on the weight value,
iv) output the animal age value based at least in part on the gingivitis value,
v) output the animal age value based at least in part on the periodontitis value, and/or
vi) output the animal age value based at least in part on the geographical information.
Regarding claim 47 Aliper teaches the invention as claimed and detailed above with respect to claim 24. Aliper also teaches:
A computer program comprising executable instructions stored in a non-transitory computer-readable medium that when executed by a processor causes the processor to perform the method of claim 24 (See at least [0018] via”...processing the taxonomic profile of the microbiota of each reference subject with a computer configured with a machine learning platform in order to predict the biological age of each reference subject; and saving the predicted biological age and associated taxonomic profile for each reference subject in a reference database with the predicted biological age associated with the taxonomic profile of the microbiota for each reference subject. In some aspects, the method includes: generating a computer program product stored on a tangible, non-transitory memory device of a computer that when executed cause the computer to: access the reference database; compare the subject's taxonomic profile with the reference database; provide information on at least one specific microorganism that modulates the predicted biological age associated of the subject; generate the report with the provided information; and cause the report to be provided to the subject...”)
Regarding claim 98 Aliper teaches:
obtaining training data for a plurality of animals, wherein: (See at least [0063] via”...the subject is often a human, other mammals, such as farm animals.. dogs, cats, as well as other types of animals, such as birds, reptiles, insects etc., can also be used as subjects”; in addition see at least [0214] via”... creating input vectors based on the microbe abundance signature..”; in addition see at least [0228] via”... A method 1400 of constructing a microbiomic aging clock is provided, as described by the following steps and shown in FIG. 14. The method includes obtaining nucleic acid information contained within the guts of a cohort of subjects (block 1402). This can be done by obtaining the nucleic acids from a biological sample of each subject and performing the assays to get the information, or the information may be obtained elsewhere and the nucleic acid information is provided for this method...” )
the training data indicates an amount of a type of bacteria that is present within a sample for each animal from among the plurality of animals; (See at least [0214] via”... creating input vectors based on the microbe abundance signature..”; in addition see at least [0228] via”... A method 1400 of constructing a microbiomic aging clock is provided, as described by the following steps and shown in FIG. 14. The method includes obtaining nucleic acid information contained within the guts of a cohort of subjects (block 1402). This can be done by obtaining the nucleic acids from a biological sample of each subject and performing the assays to get the information...”; in addition see at least [0230] via”... The method 1400 can include inferring microbe abundance profiles from nucleic acid information (block 1406)...; )and
the plurality of animals are members of the canid family; (See at least [0063] via”...the subject is often a human, other mammals, such as ..dogs..”)
associating the training data with animal age values, wherein associating the training data with the animal age values comprises associating each animal from among the second plurality of animals with an animal age value; (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... current body mass index for subject.... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others...”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”) and
training a machine learning model using the training data that is associated with the animal age values, wherein the machine learning model is configured to: : (See at least [0019] via: “...inputting the input vectors into the machine learning platform..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
receive input data for an animal; (See at least [0063] via”... the subject is often a human, other mammals, such as farm animals.. dogs, cats, as well as other types of animals, such as birds, reptiles, insects etc., can also be used as subjects”; in addition see at least [0214] via”... creating input vectors based on the microbe abundance signature..”; in addition see at least [0228] via”...A method 1400 of constructing a microbiomic aging clock is provided, as described by the following steps and shown in FIG. 14. The method includes obtaining nucleic acid information contained within the guts of a cohort of subjects (block 1402). This can be done by obtaining the nucleic acids from a biological sample of each subject and performing the assays to get the information,...”)and
output an animal age value based at least in part on the input data for the animal, wherein the animal age value identifies a predicted age for the animal. (See at least [0019] via”...generating a predicted biological aging clock of the microbiota based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the microbiota; and preparing the report to include the biological aging clock and identifies the predicted biological age of the subject based on the microbiota..”; in addition see at least [0068] via”... creating a metagenomics biological clock and the use of it to predict a subject's biological age...”)
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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
non-obviousness.
Claims 5-6 are rejected under 35 U.S.C. 103 as being un-patentable by Aliper in view of Li et.al (US 20140335534 A1) hereinafter “Li”.
Regarding claim 5 Aliper teaches the invention as claimed and detailed above with respect to claim 1. Aliper also teaches:
the machine learning model is further configured to output the animal age value based at least in part on the gingivitis value. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. ... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”),
However Aliper does not disclose the following limitation that is taught by Li:
the input data for the animal further comprises a gingivitis value for the animal; the gingivitis value is associated with a time to bleeding when probing a mouth of the animal; (See at least [0167] via”...Gingivitis is assessed using Bleeding on Probing (BOP) ... as clinical measurements. BOP frequency .. are recorded for each subject. ... Specifically, probing is performed by a dentist on the mesiobuccal and the distolingual of each tooth, for a maximum of 56 sites. Scores range from 0 to 5, with 0 assigned for normal appearing and healthy gingival up to a score of 5 for spontaneous bleeding (without provocation)...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Aliper with Li. Aliper teaches predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject that includes processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject. However, Aliper fails to disclose assessment of gingivitis based on bleeding on probing as taught by Li. Combining Aliper and Li is helpful in determining the age of the animal based on an evaluation of gingivitis.
Regarding claim 6 Aliper teaches the invention as claimed and detailed above with respect to claim 1. Aliper also teaches:
the machine learning model is further configured to output the animal age value based at least in part on the periodontitis value. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
However Aliper does not disclose the following limitation that is taught by Li:
the input data for the animal further comprises a periodontitis value for the animal; the periodontitis value is associated with an amount of periodontitis that is present in a mouth of the animal; (See at least [0006] via”... chronic gingivitis can progress to periodontitis, which is an irreversible periodontal infection characterized by alveolar bone loss, attachment loss, formation of periodontal pockets, and eventually tooth loss..;”; in addition see at least [0162] via”... severe periodontal disease, as characterized by purulent exudates, generalized mobility, and/or severe recession..”; in addition see at least [0228] via”... In the identified "gingivitis-driver" genera, several species (e.g., Tannerella forsythensis, Peptostreptococcus micros, Fusobacterium nucleatum subsp., Haemophilus paraphrophilus and Capnocytophaga sp. oral clone CZ006 et. al.) are reportedly associated with periodontitis. In addition, those potential markers of severe gingivitis the present inventors identify (e.g. Tannerella, Treponema species and the TM7 phylum) are reportedly enriched in periodontitis. Furthermore, several potential markers of Type II hosts (e.g. Selenomonas, Peptostreptococcus, unclassified Lachnospiraceae, Veillonellaceae and Oribacterium), which exhibit higher disease acuteness and susceptibility to reoccurrence, are found to be enriched in periodontitis...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Aliper with Li. Aliper teaches predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject that includes processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject. However, Aliper fails to disclose assessment of periodontitis as taught by Li. Combining Aliper and Li is helpful in determining the age of the animal based on an evaluation of periodontitis.
Claims 8, 11 are rejected under 35 U.S.C. 103 as being un-patentable by Aliper in view of in view of Kamisato et.al (JP 2010081950 A) hereinafter “Kamisato”.
Regarding claim 8 Aliper teaches the invention as claimed and detailed above with respect to claim 1. However Aliper is silent the following claim that is taught by Kamisato:
wherein the sample is collected while the animal is conscious (See at least [Page 5, lines 36-37] via”... pain in endoscopic submucosal dissection under conscious sedation is characterized by measuring amylase in a collected saliva sample..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Aliper with Kamisato. Aliper teaches predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject that includes processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject. However, Aliper fails to disclose collection of samples under consciousness as taught by Kamisato. Combining Aliper and Kamisato is helpful in taking samples from an animal that does not need to be anesthesized in order to retrieve a sample.
Regarding claim 11 Aliper teaches the invention as claimed and detailed above with respect to claim 1. However Aliper is silent the following claim that is taught by Kamisato:
wherein the sample is collected while the animal is unconscious. (See at least [Page 7, line 45] via”... saliva collected from a human being subjected to sedation..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Aliper with Kamisato. Aliper teaches predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject that includes processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject. However, Aliper fails to disclose collection of samples under sedation as taught by Kamisato. Combining Aliper and Kamisato is helpful in taking samples from an animal that does need to be sedated in order to retrieve a sample.
Claims 9-10, 12-14 are rejected under 35 U.S.C. 103 as being un-patentable by Aliper in view of Kamisato; in further view of in view of Li.
Regarding claim 9 Aliper and Kamisato in combination teach the invention as claimed and detailed above with respect to claim 8. However the combination is silent the following claim that is taught by Li:
wherein the sample comprises bacteria from a gingival area in a mouth of the animal. (See at least [0056] via”... "sample", "oral sample", or "biological sample" is a biological material isolated from a subject for analysis according to the present methods, such as ... gingival crevicular fluid (GCF)...”)
Regarding claim 10 Aliper and Kamisato in combination teach the invention as claimed and detailed above with respect to claim 8. However the combination is silent the following claim that is taught by Li:
wherein the sample comprises bacteria from a supragingival area in a mouth of the animal. (See at least [0056] via”...... "sample", "oral sample", or "biological sample" is a biological material isolated from a subject for analysis according to the present methods, such as ... supragingival plaque,..”; in addition see at least [0169] via”... Supragingival Plaque Sampling...”; in addition see at least [0170] via”... Supragingival plaque samples from each subject are collected..”)
Regarding claim 12 Aliper and Kamisato in combination teach the invention as claimed and detailed above with respect to claim 11. However the combination is silent the following claim that is taught by Li:
wherein the sample comprises bacteria from a gingival area in a mouth of the animal. (See at least [0056] via”... "sample", "oral sample", or "biological sample" is a biological material isolated from a subject for analysis according to the present methods, such as ... gingival crevicular fluid (GCF)...”)
Regarding claim 13 Aliper and Kamisato in combination teach the invention as claimed and detailed above with respect to claim 11. However the combination is silent the following claim that is taught by Li:
wherein the sample comprises bacteria from a subgingival area in a mouth of the animal. (See at least [0056] via”... "sample", "oral sample", or "biological sample" is a biological material isolated from a subject for analysis according to the present methods, such as .. subgingival plaque, ...”; in addition see at least [0064] via”... The plaque sample can be from various locations. For example, the plaque sample can be selected from the group consisting of a supragingival plaque sample, a subgingival plaque sample..”)
Regarding claim 14 Aliper and Kamisato in combination teach the invention as claimed and detailed above with respect to claim 11. However the combination is silent the following claim that is taught by Li:
wherein the sample comprises bacteria from a supragingival area in a mouth of the animal. (See at least [0056] via”... "sample", "oral sample", or "biological sample" is a biological material isolated from a subject for analysis according to the present methods, such as ... supragingival plaque,..”; in addition see at least [0169] via”... Supragingival Plaque Sampling...”; in addition see at least [0170] via”... Supragingival plaque samples from each subject are collected..”)
The motivation relating to claims 9-10 and 12-14 is as follows: It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Aliper with Li. Aliper teaches predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject that includes processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject. However, Aliper fails to disclose samples including gingival crevicular fluid, supragingival Plaque, subgingival plaque as taught by Li. Combining Aliper and Li is helpful in determining the age of the animal based on an evaluation of samples taken from the gingival, supragingival and subgingival areas.
Claim 19 is rejected under 35 U.S.C. 103 as being un-patentable by Aliper in view of Kooijman et.al (EP 3553522 A1) hereinafter “Kooijman”
Regarding claim 19 Aliper teaches the invention as claimed and detailed above with respect to claim 1 and 15. However Aliper is silent the following claim that is taught by Kooijman:
associate the training data with gingivitis values before training the machine learning model, wherein associating the training data with the gingivitis values comprises associating each animal from among the second plurality of animals with a gingivitis value. (See at least [0062] via”... threshold value can also be determined on the basis of measuring the concentration(s) of the present biomarker protein(s) in a set of samples, including patients with a known diagnosis of gingivitis and "not" gingivitis. Thereby the measured concentration values can be subjected to statistical analysis, possibly including machine learning methods, allowing to discriminate, with the desired sensitivity and specificity, patients classified as gingivitis and patients classified as not suffering from gingivitis. Therefrom, the desired threshold value can be obtained. On the basis of this threshold value, a sample to be tested can be subjected to the same concentration measurement, and the concentration values are then processed, in the same manner in which the threshold value is obtained, so as to determine a joint concentration value that can be compared with the threshold, thus allowing the tested sample to be classified as having gingivitis or not...’; in addition see at least [0079] via”... training procedure: Select N1 subjects with gingivitis (as identified by a dentist via the current criteria) and N2 subjects without gingivitis (having healthy gums). Take a saliva sample from each subject and determine the protein concentrations of a combination of biomarkers as explained above. Define the score S to be 1 for gingivitis, and 0 for no gingivitis (healthy gums). Fit the sigmoid function to the scores and protein concentration values...”; in addition see at least [0080] via”... regression or machine learning methods (linear regression, neural network, support vector machine) may be used where the score S, is high for gingivitis patients and low for the non-gingivitis/healthy controls...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Aliper with Kooijman. Aliper teaches predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject that includes processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject. However, Aliper fails to disclose use of biomarker protein(s) in a set of training samples, from patients with a known diagnosis of gingivitis and "not" gingivitis used to train a machine learning model as taught by Kooijman. Combining Aliper and Kooijman is helpful in determining efficiently whether the animal suffers or not from gingivitis based on imputing sample data to a machine learning model that has been previously trained.
Claim 20 is rejected under 35 U.S.C. 103 as being un-patentable by Aliper in view of Rmaile et.al (EP 3553518 A1) hereinafter “Rmaile”
Regarding claim 20 Aliper teaches the invention as claimed and detailed above with respect to claim 1 and 15. However Aliper is silent the following claim that is taught by Rmaile:
associate the training data with periodontitis values before training the machine learning model, wherein associating the training data with the periodontitis values comprises associating each animal from among the second plurality of animals with a periodontitis value. (See at least [0067] via”... The threshold value can also be determined on the basis of measuring the concentration(s) of the present biomarker protein(s) in a set of samples, including patients with a known diagnosis of periodontitis and "not" periodontitis. Thereby the measured concentration values can be subjected to statistical analysis, possibly including machine learning methods, allowing to discriminate, with the desired sensitivity and specificity, patients classified as periodontitis and patients classified as not suffering from periodontitis. Therefrom, the desired threshold value can be obtained. On the basis of this threshold value, a sample to be tested can be subjected to the same concentration measurement, and the concentration values are then processed, in the same manner in which the threshold value is obtained, so as to determine a joint concentration value that can be compared with the threshold, thus allowing the tested sample to be classified as having periodontitis or not...”; in addition see at least [0084] via”... training procedure: Select N1 subjects with periodontitis (as identified by a dentist via the current criteria) and N2 subjects without periodontitis (having healthy gums or gingivitis). Take a saliva sample from each subject and determine the protein concentrations of a combination of biomarkers as explained above. Define the score S to be 1 for periodontitis, and 0 for non-periodontitis (healthy gums or gingivitis). Fit the sigmoid function to the scores and protein concentration values...”; in addition see at least [0085] via”... regression or machine learning methods (linear regression, neural network, support vector machine) may be used where the score S, is high for periodontitis patients and low for the non-periodontitis/healthy controls...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Aliper with Rmaile. Aliper teaches predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject that includes processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject. However, Aliper fails to disclose use of biomarker protein(s) in a set of training samples, from patients with a known diagnosis of periodontitis and "not" periodontitis used to train a machine learning model as taught by Rmaile. Combining Aliper and Rmaile is helpful in determining efficiently whether the animal suffers or not from periodontitis based on imputing sample data to a machine learning model that has been previously trained.
Claims 39 and 99 are rejected under 35 U.S.C. 103 as being un-patentable by Aliper in view of Kooijman, in further view of Rmaile
Regarding claims 39 and 99 Aliper teaches the invention as claimed and detailed above with respect to claims 24 and 98 respectively. Aliper also teaches:
a) associating the training data with animal size classification values before training the machine learning model, wherein associating the training data with the animal size classification values comprises associating each animal from among the second plurality of animals with an animal size classification value; (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... current body mass index for subject.... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others...”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
b) associating the training data with animal breed identifiers before training the machine learning model, wherein associating the training data with the animal breed identifiers comprises associating each animal from among the second plurality of animals with an animal breed identifier (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include biometrics of subject ...The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
c) associating the training data with weight values before training the machine learning model, wherein associating the training data with the weight values comprises associating each animal from among the second plurality of animals with a weight value; (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ..., current weight and/or weight history for subject,... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
f) associating the training data with geographic location information before training the machine learning model, wherein associating the training data with the geographic location information comprises associating each animal from among the second plurality of animals with a physical location. (See at least [0085] via”... obtaining personal information regarding the subject, and including the personal information in the processing in order to determine the subject's biological age. The personal information can include ... geographical location .. for subject and/or family members, ... The personal data can then be compared with the metagenomics profile of the subject or of reference metagenomics profiles during the determination of biological age. This information can be used to explore correlations between a predicted biological age and the data of the taxonomic profiles in view of the personal data. When done across multiple profiles, the correlations of personal data with the predicted age and the data of the taxonomic profiles can be used to enhance the predictions as well as determine unhealthy personal data that can contribute to higher phenotypic ages, or use the healthy personal data to design strategies (e.g., treatment, diet, exercise, lifestyle, etc.) to reduce the biological age in others..”; in addition see at least [0068] via”...Data related to metagenomics taxonomical profiles based on genetic analyses correlated with actual ages of subjects can be used to train computer models (e.g., the machine learning and deep learning techniques with one or more deep neural networks). The data can be input into such computer models to train the computer models to correlate the data with ages, and thereby be able to receive data and correlate the data with the chronological age and/or phenotypic age. For example, the model can be trained such that when a certain data set is used for analysis, the model can provide output of a predicted age based on the biomarkers and taxonomic profiles matching with certain ages...”)
However Aliper is silent the following limitation that is taught by Kooijman:
d) associating the training data with gingivitis values before training the machine learning model, wherein associating the training data with the gingivitis values comprises associating each animal from among the second plurality of animals with a gingivitis value; (See at least [0062] via”... threshold value can also be determined on the basis of measuring the concentration(s) of the present biomarker protein(s) in a set of samples, including patients with a known diagnosis of gingivitis and "not" gingivitis. Thereby the measured concentration values can be subjected to statistical analysis, possibly including machine learning methods, allowing to discriminate, with the desired sensitivity and specificity, patients classified as gingivitis and patients classified as not suffering from gingivitis. Therefrom, the desired threshold value can be obtained. On the basis of this threshold value, a sample to be tested can be subjected to the same concentration measurement, and the concentration values are then processed, in the same manner in which the threshold value is obtained, so as to determine a joint concentration value that can be compared with the threshold, thus allowing the tested sample to be classified as having gingivitis or not...’; in addition see at least [0079] via”... training procedure: Select N1 subjects with gingivitis (as identified by a dentist via the current criteria) and N2 subjects without gingivitis (having healthy gums). Take a saliva sample from each subject and determine the protein concentrations of a combination of biomarkers as explained above. Define the score S to be 1 for gingivitis, and 0 for no gingivitis (healthy gums). Fit the sigmoid function to the scores and protein concentration values...”; in addition see at least [0080] via”... regression or machine learning methods (linear regression, neural network, support vector machine) may be used where the score S, is high for gingivitis patients and low for the non-gingivitis/healthy controls...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Aliper with Kooijman. Aliper teaches predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject that includes processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject. However, Aliper fails to disclose use of biomarker protein(s) in a set of training samples, from patients with a known diagnosis of gingivitis and "not" gingivitis used to train a machine learning model as taught by Kooijman. Combining Aliper and Kooijman is helpful in determining efficiently whether the animal suffers or not from gingivitis based on imputing sample data to a machine learning model that has been previously trained.
However Aliper and Kooijman are silent the following limitation that is taught by Rmaile:
e) associating the training data with periodontitis values before training the machine learning model, wherein associating the training data with the periodontitis values comprises associating each animal from among the second plurality of animals with a periodontitis value; (See at least [0067] via”... The threshold value can also be determined on the basis of measuring the concentration(s) of the present biomarker protein(s) in a set of samples, including patients with a known diagnosis of periodontitis and "not" periodontitis. Thereby the measured concentration values can be subjected to statistical analysis, possibly including machine learning methods, allowing to discriminate, with the desired sensitivity and specificity, patients classified as periodontitis and patients classified as not suffering from periodontitis. Therefrom, the desired threshold value can be obtained. On the basis of this threshold value, a sample to be tested can be subjected to the same concentration measurement, and the concentration values are then processed, in the same manner in which the threshold value is obtained, so as to determine a joint concentration value that can be compared with the threshold, thus allowing the tested sample to be classified as having periodontitis or not...”; in addition see at least [0084] via”... training procedure: Select N1 subjects with periodontitis (as identified by a dentist via the current criteria) and N2 subjects without periodontitis (having healthy gums or gingivitis). Take a saliva sample from each subject and determine the protein concentrations of a combination of biomarkers as explained above. Define the score S to be 1 for periodontitis, and 0 for non-periodontitis (healthy gums or gingivitis). Fit the sigmoid function to the scores and protein concentration values...”; in addition see at least [0085] via”... regression or machine learning methods (linear regression, neural network, support vector machine) may be used where the score S, is high for periodontitis patients and low for the non-periodontitis/healthy controls...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Aliper and Kooijman with Rmaile. Aliper teaches predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject that includes processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject. However, Aliper fails to disclose use of biomarker protein(s) in a set of training samples, from patients with a known diagnosis of periodontitis and "not" periodontitis used to train a machine learning model as taught by Rmaile. Combining Aliper and Rmaile is helpful in determining efficiently whether the animal suffers or not from periodontitis based on imputing sample data to a machine learning model that has been previously trained.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
TENG (CN 106202989 A) – A Method Of Obtaining Individual Children Biological Age Based On Oral Microbial Community - teaches: The invention claims a method for obtaining individual children biological age based on oral microbial community, the method comprising obtaining a sample of the child individual oral microorganisms, extracting the oral microbial DNA, the DNA information into microbial community information by using a random forest algorithm, oral microbial community information and age regression analysis, constructing a regression model to obtain the Chinese public individual children age. The project provided by the invention can accurately obtain the biological age of Chinese people individual children, can simply and fast without tamper resistance, obtaining buccal saliva or plaque sample, the individual children age term detection, which is good for quickly judging the host physical healthy state, provides a clue for health monitoring and improve the disease early diagnosis rate.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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