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 .
Remarks
In response to communications sent January 27, 2023 claim(s) 1-27 are pending in this application; of these claims 1, 10, and 19 are in independent form.
Priority
Regarding the filings dates of each individual claim: The originally filed claims of the instant application are identical to the originally filed claims of the parent application 18/060,382, despite that additional description and support was added to the specification of the instant application. However, because the prior art cited is more than one year before the earliest provisional patent application in the patent family, the Examiner may defer determination of the precise filing dates of each individual claim to a later time in prosecution.
Drawings
The drawing(s) filed on January 27, 2023 are accepted by the Examiner.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the two Information Disclosure Statements(s) filed on: January 13, 2025.
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 1-27 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematics followed by a mental process based on the mathematics, which is a combination of judicial exceptions. This judicial exception is not integrated into a practical application because the additional elements are insignificant extra-solution activity that are needed to carry out all uses of the judicial exceptions. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because inputting and outputting data is well-understood, routine, and conventional uses of a general purpose computer (“apply it”).
See the further explanation of the rejection of the depend claims and detailed clarification of the independent claims, see below:
1. A computer-implemented method comprising:
(a) accessing gut compositional data including a taxonomic abundance, a taxonomic diversity, and/or an enterotype for a subject (this element is not an abstract idea, but is necessary pre-solution activity for the abstract idea; it part of applying the judicial exception on a general purpose computer);
(b) generating a gut microbiome signature for a safety of a statin therapy for the subject and an efficacy of the statin therapy for the subject by applying a classifier to the gut compositional data, the safety of the statin therapy characterized by an insulin resistance of the subject, and the efficacy of the statin therapy characterized by a blood hydroxymethylglutarate (HMG) level of the subject (paragraph 40 of applicant’s specification recites that a classier may be a logistic regression model; therefore, this element is mathematics, which is a judicial exception regardless of whether it can be performed in the human mind);
(c) determining a recommended therapy for the subject based on the gut microbiome signature and one or more taxa of the gut compositional data of the subject, the recommended therapy selected from a statin therapy intensity, a probiotic therapy, a prebiotic therapy, or a combination thereof (mental process, specifically a judgement, that can be performed in the human mind); and
(d) outputting the recommended therapy (not an abstract idea, but necessary post-solution activity for carrying out all uses of the abstract idea).
2. The computer-implemented method of claim 1, wherein determining the recommended therapy comprises:
comparing the gut microbiome signature and the gut compositional data of the subject to a reference dataset, the reference dataset comprising a plurality of gut microbiome data and blood metabolite data of a reference population exhibiting variable insulin resistance and blood HMG level responses to a given statin therapy intensity (comparisons are a mental process that is performable in the human mind).
3. The computer-implemented method of claim 1, further comprising:
determining a presence of Akkermansia for the subject is below a first threshold based on the gut compositional data (the determination is a mental process that can be performed in the human mind); and
facilitating the probiotic therapy and/or the prebiotic therapy for the subject based on the presence of Akkermansia being below the first threshold (the broadest reasonable interpretation of the step of facilitating encompasses mental communication of a recommendation and does not necessarily involve administering the therapy; therefore, the claims encompass the abstract idea of a mental process of recommending).
4. The computer-implemented method of claim 1, further comprising:
determining the blood HMG level for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
generating the gut microbiome signature for the subject by applying the classifier to the gut compositional data and the blood HMG level (paragraph 40 of applicant’s specification recites that a classier may be a logistic regression model; therefore, this element is mathematics, which is a judicial exception regardless of whether it can be performed in the human mind).
5. The computer-implemented method of claim 1, further comprising:
accessing fecal nucleic acid sequence data and/or blood metabolite data for the subject (accessing data is an additional element, but it is necessary pre-solution activity to carry out the judicial exception and is well-understood, routine, and convention by simply applying the judicial exception on a general purpose computer); and
generating the gut compositional data for the subject based on the fecal nucleic acid sequence data and/or the blood metabolite data (the generation step encompasses a mental process of computing genetic information using sequence data and therefore is part of the judicial exception).
6. The computer-implemented method of claim 1, wherein determining the recommended therapy comprises one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
determining the statin therapy intensity is below a threshold intensity (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea).
7. The computer-implemented method of claim 1, wherein determining the recommended therapy comprises one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining at least one of: (i) a presence of Akkermansia for the subject, (ii) an insulin resistance characterization for the subject, or (iii) a treatment for insulin resistance for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
determining the statin therapy intensity is above a threshold intensity (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea).
8. The computer-implemented method of claim 1, wherein determining the recommended therapy comprises one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. below a first threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining that the enterotype indicated by the gut compositional data excludes a Bacteroides enterotype (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining the gut compositional data includes an alpha-diversity greater than a second threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
determining the statin therapy intensity is greater than a threshold intensity (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea).
9. The computer-implemented method of claim 1, further comprising:
determining a genetic risk score associated with the subject having one or more alleles associated with the efficacy of the statin therapy for the subject or the safety of the statin therapy for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
generating the gut microbiome signature for the subject by applying the classifier to the gut compositional data and the genetic risk score (paragraph 40 of applicant’s specification recites that a classier may be a logistic regression model; therefore, this element is mathematics, which is a judicial exception regardless of whether it can be performed in the human mind).
10. A system comprising:
one or more data processors (an additional element beyond the abstract idea, but part of applying the judicial exception on a general purpose computer); and
a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors (an additional element beyond the abstract idea, but part of applying the judicial exception on a general purpose computer), cause the one or more data processors to perform a set of actions including:
(a) accessing gut compositional data including a taxonomic abundance, a taxonomic diversity, and/or an enterotype for a subject (this element is not an abstract idea, but is necessary pre-solution activity for the abstract idea; it part of applying the judicial exception on a general purpose computer);
(b) generating a gut microbiome signature for a safety of a statin therapy for the subject and an efficacy of the statin therapy for the subject by applying a classifier to the gut compositional data, the safety of the statin therapy characterized by an insulin resistance of the subject, and the efficacy of the statin therapy characterized by a blood hydroxymethylglutarate (HMG) level of the subject (paragraph 40 of applicant’s specification recites that a classier may be a logistic regression model; therefore, this element is mathematics, which is a judicial exception regardless of whether it can be performed in the human mind);
(c) determining a recommended therapy for the subject based on the gut microbiome signature and one or more taxa of the gut compositional data of the subject, the recommended therapy selected from a statin therapy intensity, a probiotic therapy, a prebiotic therapy, or a combination thereof (mental process, specifically a judgement, that can be performed in the human mind); and
(d) outputting the recommended therapy (not an abstract idea, but necessary post-solution activity for carrying out all uses of the abstract idea).
11. The system of claim 10, wherein the set of actions further include determining the recommended therapy by:
comparing the gut microbiome signature and the gut compositional data of the subject to a reference dataset, the reference dataset comprising a plurality of gut microbiome data and blood metabolite data of a reference population exhibiting variable insulin resistance and blood HMG level responses to a given statin therapy intensity (comparisons are a mental process that is performable in the human mind).
12. The system of claim 10, wherein the set of actions further includes:
determining a presence of Akkermansia for the subject is below a first threshold based on the gut compositional data (the determination is a mental process that can be performed in the human mind); and
facilitating the probiotic therapy and/or the prebiotic therapy for the subject based on the presence of Akkermansia being below the first threshold (the broadest reasonable interpretation of the step of facilitating encompasses mental communication of a recommendation and does not necessarily involve administering the therapy; therefore, the claims encompass the abstract idea of a mental process of recommending).
13. The system of claim 10, wherein the set of actions further includes:
determining the blood HMG level for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
generating the gut microbiome signature for the subject by applying the classifier to the gut compositional data and the blood HMG level (paragraph 40 of applicant’s specification recites that a classier may be a logistic regression model; therefore, this element is mathematics, which is a judicial exception regardless of whether it can be performed in the human mind).
14. The system of claim 10, wherein the set of actions further includes:
accessing fecal nucleic acid sequence data and/or blood metabolite data for the subject (accessing data is an additional element, but it is necessary pre-solution activity to carry out the judicial exception and is well-understood, routine, and convention by simply applying the judicial exception on a general purpose computer); and
generating the gut compositional data for the subject based on the fecal nucleic acid sequence data and/or the blood metabolite data (the generation step encompasses a mental process of computing genetic information using sequence data and therefore is part of the judicial exception).
15. The system of claim 10, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
determining the statin therapy intensity is below a threshold intensity (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea).
16. The system of claim 10, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining at least one of: (i) a presence of Akkermansia for the subject, (ii) an insulin resistance characterization for the subject, or (iii) a treatment for insulin resistance for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
determining the statin therapy intensity is above a threshold intensity (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea).
17. The system of claim 10, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. below a first threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining that the enterotype indicated by the gut compositional data excludes a Bacteroides enterotype (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining the gut compositional data includes an alpha-diversity greater than a second threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
determining the statin therapy intensity is greater than a threshold intensity (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea).
18. The system of claim 10, wherein the set of actions further include:
determining a genetic risk score associated with the subject having one or more alleles associated with the efficacy of the statin therapy for the subject or the safety of the statin therapy for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
generating the gut microbiome signature for the subject by applying the classifier to the gut compositional data and the genetic risk score (paragraph 40 of applicant’s specification recites that a classier may be a logistic regression model; therefore, this element is mathematics, which is a judicial exception regardless of whether it can be performed in the human mind).
19. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium (an additional element beyond the abstract idea, but part of applying the judicial exception on a general purpose computer), including instructions configured to cause one or more data processors to perform a set of actions including:
(a) accessing gut compositional data including a taxonomic abundance, a taxonomic diversity, and/or an enterotype for a subject (an additional element beyond the abstract idea, but part of applying the judicial exception on a general purpose computer);
(b) generating a gut microbiome signature for a safety of a statin therapy for the subject and an efficacy of the statin therapy for the subject by applying a classifier to the gut compositional data, the safety of the statin therapy characterized by an insulin resistance of the subject, and the efficacy of the statin therapy characterized by a blood hydroxymethylglutarate (HMG) level of the subject (paragraph 40 of applicant’s specification recites that a classier may be a logistic regression model; therefore, this element is mathematics, which is a judicial exception regardless of whether it can be performed in the human mind);
(c) determining a recommended therapy for the subject based on the gut microbiome signature and one or more taxa of the gut compositional data of the subject, the recommended therapy selected from a statin therapy intensity, a probiotic therapy, a prebiotic therapy, or a combination thereof (mental process, specifically a judgement, that can be performed in the human mind); and
(d) outputting the recommended therapy (not an abstract idea, but necessary post-solution activity for carrying out all uses of the abstract idea).
20. The computer-program product of claim 19, wherein the set of actions further include determining the recommended therapy by:
comparing the gut microbiome signature and the gut compositional data of the subject to a reference dataset, the reference dataset comprising a plurality of gut microbiome data and blood metabolite data of a reference population exhibiting variable insulin resistance and blood HMG level responses to a given statin therapy intensity (comparisons are a mental process that is performable in the human mind).
21. The computer-program product of claim 19, wherein the set of actions further includes:
determining a presence of Akkermansia for the subject is below a first threshold based on the gut compositional data (the determination is a mental process that can be performed in the human mind); and
facilitating the probiotic therapy and/or the prebiotic therapy for the subject based on the presence of Akkermansia being below the first threshold (the broadest reasonable interpretation of the step of facilitating encompasses mental communication of a recommendation and does not necessarily involve administering the therapy; therefore, the claims encompass the abstract idea of a mental process of recommending).
22. The computer-program product of claim 19, wherein the set of actions further includes:
determining the blood HMG level for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
generating the gut microbiome signature for the subject by applying the classifier to the gut compositional data and the blood HMG level (paragraph 40 of applicant’s specification recites that a classier may be a logistic regression model; therefore, this element is mathematics, which is a judicial exception regardless of whether it can be performed in the human mind).
23. The computer-program product of claim 19, wherein the set of actions further includes:
accessing fecal nucleic acid sequence data and/or blood metabolite data for the subject (accessing data is an additional element, but it is necessary pre-solution activity to carry out the judicial exception and is well-understood, routine, and convention by simply applying the judicial exception on a general purpose computer); and
generating the gut compositional data for the subject based on the fecal nucleic acid sequence data and/or the blood metabolite data (the generation step encompasses a mental process of computing genetic information using sequence data and therefore is part of the judicial exception).
24. The computer-program product of claim 19, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
determining the statin therapy intensity is below a threshold intensity (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea).
25. The computer-program product of claim 19, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining at least one of: (i) a presence of Akkermansia for the subject, (ii) an insulin resistance characterization for the subject, or (iii) a treatment for insulin resistance for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
determining the statin therapy intensity is above a threshold intensity (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea).
26. The computer-program product of claim 19, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. below a first threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining that the enterotype indicated by the gut compositional data excludes a Bacteroides enterotype (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea);
determining the gut compositional data includes an alpha-diversity greater than a second threshold for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
determining the statin therapy intensity is greater than a threshold intensity (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea).
27. The computer-program product of claim 19, wherein the set of actions further include:
determining a genetic risk score associated with the subject having one or more alleles associated with the efficacy of the statin therapy for the subject or the safety of the statin therapy for the subject (the broadest reasonable interpretation of “determining” encompasses a mental determination of a conclusive level from raw measurements; therefore, the step encompasses an abstract idea); and
generating the gut microbiome signature for the subject by applying the classifier to the gut compositional data and the genetic risk score (paragraph 40 of applicant’s specification recites that a classier may be a logistic regression model; therefore, this element is mathematics, which is a judicial exception regardless of whether it can be performed in the human mind).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-12, 14-21, and 23-27is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2021/0166797 A1 (“Neumann”).
As to claim 1, Neumann teaches a computer-implemented method comprising:
(a) accessing gut compositional data including a taxonomic abundance, a taxonomic diversity, and/or an enterotype for a subject (Neumann Para [0101]: accessing a microbiome profile for a user profile of a subject);
(b) generating a gut microbiome signature for a safety (Neumann Para [0121]: generating efficacy profiles, including a toxicity risk) of a statin therapy (Neumann Para [0121]: for fluvastatin) for the subject (Neumann [0121]: for the user) and an efficacy of the statin therapy for the subject (Neumann Para [0121]: generating efficacy profiles, including an expected response) by applying a classifier (Neumann Para [0081]: applying a machine-learning algorithm; according to Para [0085]: a classifier is one embodiment of the machine-learning algorithm) to the gut compositional data (Neumann Para [0101]: based on a microbiome profile), the safety of the statin therapy characterized by an insulin resistance of the subject (the characterization is a property of safety, and the safety element has been mapped), and the efficacy of the statin therapy characterized by a blood hydroxymethylglutarate (HMG) level of the subject (the characterization is an property of efficacy, and the efficacy element has been mapped);
(c) determining a recommended therapy for the subject (Neumann Para [0121]: generating a recommendation for rosuvastatin instead of fluvastatin) based on the gut microbiome signature and one or more taxa of the gut compositional data of the subject (Neumann Para [0101]: based on a microbiome profile for a user profile of a subject; Figure 6 elements 610 and the figure as a whole illustrates the basis of the recommendation lies in the profile);, the recommended therapy selected from a statin therapy intensity (Neumann Para [0121]: recommended therapy is a statin, rosuvastatin), a probiotic therapy, a prebiotic therapy, or a combination thereof (these elements are claimed in the alternative and do not need to all be mapped); and
(d) outputting the recommended therapy (Neumann Para [0121]: providing the recommendation).
As to claim 2, Neumann teaches the computer-implemented method of claim 1, wherein determining the recommended therapy comprises:
comparing the gut microbiome signature and the gut compositional data of the subject to a reference dataset (Neumann Para [0114]: comparing the kinetic data to kinetic training data; see Para [0089] for the definition of “kinetic data” as used in the Neumann reference), the reference dataset comprising a plurality of gut microbiome data (Neumann Para [0114]: including oral gastrointestinal data) and blood metabolite data (Neumann Para [0114]: and renal metabolism data) of a reference population (Neumann Para [0114]: of a renal kinetic data set) exhibiting variable insulin resistance and blood HMG level responses to a given statin therapy intensity (Neumann Para [0114]: exhibiting prescriptive therapies for various kinetic data).
As to claim 3, Neumann teaches the computer-implemented method of claim 1, further comprising:
determining a presence of Akkermansia for the subject is below a first threshold based on the gut compositional data (Neumann Para [0047]: absence of Ackerman's muciniphila, which the Examiner assumed was another term for Akkermansia); and
facilitating the probiotic therapy and/or the prebiotic therapy for the subject based on the presence of Akkermansia being below the first threshold (Neumann Para [0100]: administering a prescriptive therapy of probiotics based on an efficacy profile).
As to claim 5, Neumann teaches the computer-implemented method of claim 1, further comprising:
accessing fecal nucleic acid sequence data (this element is claimed in the alternative and does not need to be mapped) and/or blood metabolite data for the subject (Neumann Para [0049]: accessing blood tests that identify metabolites from microorganisms); and
generating the gut compositional data for the subject based on the fecal nucleic acid sequence data and/or the blood metabolite data (Neumann Para [0101]: accessing a microbiome profile for a user profile of a subject).
As to claim 6, Neumann teaches the computer-implemented method of claim 1, wherein determining the recommended therapy comprises one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (Neumann Para [0047]: determining gut composition of that has a presence of Bacteroides);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (this element is claimed in the alternative and does not need to be mapped);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (this element is claimed in the alternative and does not need to be mapped); and
determining the statin therapy intensity is below a threshold intensity (this element is claimed in the alternative and does not need to be mapped).
As to claim 7, Neumann teaches the computer-implemented method of claim 1, wherein determining the recommended therapy comprises one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (Neumann Para [0046]: Bacteroides spp presents may be part of the microbiome analysis);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (this element is claimed in the alternative and does not need to be mapped);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (this element is claimed in the alternative and does not need to be mapped);
determining at least one of: (i) a presence of Akkermansia for the subject (Neumann Para [0047]: absence of Ackerman's muciniphila, which the Examiner assumed was another term for Akkermansia), (ii) an insulin resistance characterization for the subject (Neumann Para [0064]: insulin sensitivity as a basis), or (iii) a treatment for insulin resistance for the subject (Neumann Para [0087]: determining whether insulin treatment is recommended for the subject); and
determining the statin therapy intensity is above a threshold intensity (Neumann Para [0087]: determining whether statin treatment is compatible with the subject).
As to claim 8, Neumann teaches the computer-implemented method of claim 1, wherein determining the recommended therapy comprises one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. below a first threshold for the subject (Neumann Para [0047]: determining gut composition of that has a presence of Bacteroides);
determining that the enterotype indicated by the gut compositional data excludes a Bacteroides enterotype (this element is claimed in the alternative and does not need to be mapped);
determining the gut compositional data includes an alpha-diversity greater than a second threshold for the subject (this element is claimed in the alternative and does not need to be mapped); and
determining the statin therapy intensity is greater than a threshold intensity (Neumann Para [0087]: determining whether statin treatment is compatible with the subject).
As to claim 9, Neumann teaches the computer-implemented method of claim 1, further comprising:
determining a genetic risk score associated with the subject having one or more alleles (Neumann Para [0030]: determining biological state based on genome data) associated with the efficacy of the statin therapy for the subject or the safety of the statin therapy for the subject (Neumann Para [0121]: determining statins based on an efficacy profile); and
generating the gut microbiome signature for the subject by applying the classifier to the gut compositional data and the genetic risk score (Neumann Para [0081]: applying a machine-learning algorithm; according to Para [0085]: a classifier is one embodiment of the machine-learning algorithm).
As to claim 10, Neumann teaches a system comprising:
one or more data processors (Neumann Figure 8); and
a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors (Neumann Figure 8), cause the one or more data processors to perform a set of actions including:
(a) accessing gut compositional data including a taxonomic abundance, a taxonomic diversity, and/or an enterotype for a subject (Neumann Para [0101]: accessing a microbiome profile for a user profile of a subject);
(b) generating a gut microbiome signature for a safety (Neumann Para [0121]: generating efficacy profiles, including a toxicity risk) of a statin therapy (Neumann Para [0121]: for fluvastatin) for the subject (Neumann [0121]: for the user) and an efficacy of the statin therapy for the subject (Neumann Para [0121]: generating efficacy profiles, including an expected response) by applying a classifier (Neumann Para [0081]: applying a machine-learning algorithm; according to Para [0085]: a classifier is one embodiment of the machine-learning algorithm) to the gut compositional data (Neumann Para [0101]: based on a microbiome profile), the safety of the statin therapy characterized by an insulin resistance of the subject (the characterization is a property of safety, and the safety element has been mapped), and the efficacy of the statin therapy characterized by a blood hydroxymethylglutarate (HMG) level of the subject (the characterization is an property of efficacy, and the efficacy element has been mapped);
(c) determining a recommended therapy for the subject (Neumann Para [0121]: generating a recommendation for rosuvastatin instead of fluvastatin) based on the gut microbiome signature and one or more taxa of the gut compositional data of the subject (Neumann Para [0101]: based on a microbiome profile for a user profile of a subject; Figure 6 elements 610 and the figure as a whole illustrates the basis of the recommendation lies in the profile), the recommended therapy selected from a statin therapy intensity (Neumann Para [0121]: recommended therapy is a statin, rosuvastatin), a probiotic therapy, a prebiotic therapy, or a combination thereof (these elements are claimed in the alternative and do not need to all be mapped); and
(d) outputting the recommended therapy (Neumann Para [0121]: providing the recommendation).
As to claim 11, Neumann teaches the system of claim 10, wherein the set of actions further include determining the recommended therapy by:
comparing the gut microbiome signature and the gut compositional data of the subject to a reference dataset (Neumann Para [0114]: comparing the kinetic data to kinetic training data; see Para [0089] for the definition of “kinetic data” as used in the Neumann reference), the reference dataset comprising a plurality of gut microbiome data (Neumann Para [0114]: including oral gastrointestinal data) and blood metabolite data (Neumann Para [0114]: and renal metabolism data) of a reference population (Neumann Para [0114]: of a renal kinetic data set) exhibiting variable insulin resistance and blood HMG level responses to a given statin therapy intensity (Neumann Para [0114]: exhibiting prescriptive therapies for various kinetic data).
As to claim 12, Neumann teaches the system of claim 10, wherein the set of actions further includes:
determining a presence of Akkermansia for the subject is below a first threshold based on the gut compositional data (Neumann Para [0047]: absence of Ackerman's muciniphila, which the Examiner assumed was another term for Akkermansia); and
facilitating the probiotic therapy and/or the prebiotic therapy for the subject based on the presence of Akkermansia being below the first threshold (Neumann Para [0100]: administering a prescriptive therapy of probiotics based on an efficacy profile).
As to claim 14, Neumann teaches the system of claim 10, wherein the set of actions further includes:
accessing fecal nucleic acid sequence data (this element is claimed in the alternative and does not need to be mapped) and/or blood metabolite data for the subject (Neumann Para [0049]: accessing blood tests that identify metabolites from microorganisms); and
generating the gut compositional data for the subject based on the fecal nucleic acid sequence data and/or the blood metabolite data (Neumann Para [0101]: accessing a microbiome profile for a user profile of a subject).
As to claim 15, Neumann teaches the system of claim 10, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (Neumann Para [0047]: determining gut composition of that has a presence of Bacteroides);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (this element is claimed in the alternative and does not need to be mapped);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (this element is claimed in the alternative and does not need to be mapped); and
determining the statin therapy intensity is below a threshold intensity (this element is claimed in the alternative and does not need to be mapped).
As to claim 16, Neumann teaches the system of claim 10, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (Neumann Para [0047]: determining gut composition of that has a presence of Bacteroides);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (this element is claimed in the alternative and does not need to be mapped);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (this element is claimed in the alternative and does not need to be mapped);
determining at least one of: (i) a presence of Akkermansia for the subject (Neumann Para [0047]: absence of Ackerman's muciniphila, which the Examiner assumed was another term for Akkermansia), (ii) an insulin resistance characterization for the subject (Neumann Para [0064]: insulin sensitivity as a basis), or (iii) a treatment for insulin resistance for the subject (Neumann Para [0087]: determining whether insulin treatment is recommended for the subject); and
determining the statin therapy intensity is above a threshold intensity (Neumann Para [0087]: determining whether statin treatment is compatible with the subject).
As to claim 17, Neumann teaches the system of claim 10, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. below a first threshold for the subject (Neumann Para [0047]: determining gut composition of that has a presence of Bacteroides);
determining that the enterotype indicated by the gut compositional data excludes a Bacteroides enterotype (this element is claimed in the alternative and does not need to be mapped);
determining the gut compositional data includes an alpha-diversity greater than a second threshold for the subject (this element is claimed in the alternative and does not need to be mapped); and
determining the statin therapy intensity is greater than a threshold intensity (Neumann Para [0087]: determining whether statin treatment is compatible with the subject).
As to claim 18, Neumann teaches the system of claim 10, wherein the set of actions further include:
determining a genetic risk score associated with the subject having one or more alleles (Neumann Para [0030]: determining biological state based on genome data) associated with the efficacy of the statin therapy for the subject or the safety of the statin therapy for the subject (Neumann Para [0121]: determining statins based on an efficacy profile); and
generating the gut microbiome signature for the subject by applying the classifier to the gut compositional data and the genetic risk score (Neumann Para [0081]: applying a machine-learning algorithm; according to Para [0085]: a classifier is one embodiment of the machine-learning algorithm).
As to claim 19, Neumann teaches a computer-program product tangibly embodied in a non-transitory machine-readable storage medium (Neumann Figure 8), including instructions configured to cause one or more data processors to perform a set of actions including:
(a) accessing gut compositional data including a taxonomic abundance, a taxonomic diversity, and/or an enterotype for a subject (Neumann Para [0101]: accessing a microbiome profile for a user profile of a subject);
(b) generating a gut microbiome signature for a safety (Neumann Para [0121]: generating efficacy profiles, including a toxicity risk) of a statin therapy (Neumann Para [0121]: for fluvastatin) for the subject (Neumann [0121]: for the user) and an efficacy of the statin therapy for the subject (Neumann Para [0121]: generating efficacy profiles, including an expected response) by applying a classifier (Neumann Para [0081]: applying a machine-learning algorithm; according to Para [0085]: a classifier is one embodiment of the machine-learning algorithm) to the gut compositional data (Neumann Para [0101]: based on a microbiome profile), the safety of the statin therapy characterized by an insulin resistance of the subject (the characterization is a property of safety, and the safety element has been mapped), and the efficacy of the statin therapy characterized by a blood hydroxymethylglutarate (HMG) level of the subject (the characterization is an property of efficacy, and the efficacy element has been mapped);
(c) determining a recommended therapy for the subject (Neumann Para [0121]: generating a recommendation for rosuvastatin instead of fluvastatin) based on the gut microbiome signature and one or more taxa of the gut compositional data of the subject (Neumann Para [0101]: based on a microbiome profile for a user profile of a subject; Figure 6 elements 610 and the figure as a whole illustrates the basis of the recommendation lies in the profile), the recommended therapy selected from a statin therapy intensity (Neumann Para [0121]: recommended therapy is a statin, rosuvastatin), a probiotic therapy, a prebiotic therapy, or a combination thereof (these elements are claimed in the alternative and do not need to all be mapped); and
(d) outputting the recommended therapy (Neumann Para [0121]: providing the recommendation).
As to claim 20, Neumann teaches the computer-program product of claim 19, wherein the set of actions further include determining the recommended therapy by:
comparing the gut microbiome signature and the gut compositional data of the subject to a reference dataset (Neumann Para [0114]: comparing the kinetic data to kinetic training data; see Para [0089] for the definition of “kinetic data” as used in the Neumann reference), the reference dataset comprising a plurality of gut microbiome data (Neumann Para [0114]: including oral gastrointestinal data) and blood metabolite data (Neumann Para [0114]: and renal metabolism data) of a reference population (Neumann Para [0114]: of a renal kinetic data set) exhibiting variable insulin resistance and blood HMG level responses to a given statin therapy intensity (Neumann Para [0114]: exhibiting prescriptive therapies for various kinetic data).
As to claim 21, Neumann teaches the computer-program product of claim 19, wherein the set of actions further includes:
determining a presence of Akkermansia for the subject is below a first threshold based on the gut compositional data (Neumann Para [0047]: absence of Ackerman's muciniphila, which the Examiner assumed was another term for Akkermansia); and
facilitating the probiotic therapy and/or the prebiotic therapy for the subject based on the presence of Akkermansia being below the first threshold (Neumann Para [0100]: administering a prescriptive therapy of probiotics based on an efficacy profile).
As to claim 23, Neumann teaches the computer-program product of claim 19, wherein the set of actions further includes:
accessing fecal nucleic acid sequence data (this element is claimed in the alternative and does not need to be mapped) and/or blood metabolite data for the subject (Neumann Para [0049]: accessing blood tests that identify metabolites from microorganisms); and
generating the gut compositional data for the subject based on the fecal nucleic acid sequence data and/or the blood metabolite data (Neumann Para [0101]: accessing a microbiome profile for a user profile of a subject).
As to claim 24, Neumann teaches the computer-program product of claim 19, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (Neumann Para [0047]: determining gut composition of that has a presence of Bacteroides);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (this element is claimed in the alternative and does not need to be mapped);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (this element is claimed in the alternative and does not need to be mapped); and
determining the statin therapy intensity is below a threshold intensity (this element is claimed in the alternative and does not need to be mapped).
As to claim 25, Neumann teaches the computer-program product of claim 19, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. above a first threshold for the subject (Neumann Para [0047]: determining gut composition of that has a presence of Bacteroides);
determining that the enterotype included in the gut compositional data is a Bacteroides 1 enterotype or a Bacteroides 2 enterotype (this element is claimed in the alternative and does not need to be mapped);
determining the gut compositional data includes an alpha-diversity below a second threshold for the subject (this element is claimed in the alternative and does not need to be mapped);
determining at least one of: (i) a presence of Akkermansia for the subject (Neumann Para [0047]: absence of Ackerman's muciniphila, which the Examiner assumed was another term for Akkermansia), (ii) an insulin resistance characterization for the subject (Neumann Para [0064]: insulin sensitivity as a basis), or (iii) a treatment for insulin resistance for the subject (Neumann Para [0087]: determining whether insulin treatment is recommended for the subject); and
determining the statin therapy intensity is above a threshold intensity (Neumann Para [0087]: determining whether statin treatment is compatible with the subject).
As to claim 26, Neumann teaches the computer-program product of claim 19, wherein the set of actions further includes determining the recommended therapy by performing one or more steps selected from:
determining the gut compositional data includes a relative abundance of Bacteroides ssp. below a first threshold for the subject (Neumann Para [0047]: determining gut composition of that has a presence of Bacteroides);
determining that the enterotype indicated by the gut compositional data excludes a Bacteroides enterotype (this element is claimed in the alternative and does not need to be mapped);
determining the gut compositional data includes an alpha-diversity greater than a second threshold for the subject (this element is claimed in the alternative and does not need to be mapped); and
determining the statin therapy intensity is greater than a threshold intensity (Neumann Para [0087]: determining whether statin treatment is compatible with the subject).
As to claim 27, Neumann teaches the computer-program product of claim 19, wherein the set of actions further include:
determining a genetic risk score associated with the subject having one or more alleles (Neumann Para [0030]: determining biological state based on genome data) associated with the efficacy of the statin therapy for the subject or the safety of the statin therapy for the subject (Neumann Para [0121]: determining statins based on an efficacy profile); and
generating the gut microbiome signature for the subject by applying the classifier to the gut compositional data and the genetic risk score (Neumann Para [0081]: applying a machine-learning algorithm; according to Para [0085]: a classifier is one embodiment of the machine-learning algorithm).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wilmanski, Tomasz, et al. "Heterogeneity in statin responses explained by variation in the human gut microbiome." medRxiv (2021): 2021-12.
(Authors are similar to the inventive entity of the instant application)
US-20200227166-A1: statins, classifier, microbiome, insulin
US-20150317444-A1: metabogen and diabetes
US-20150299776-A1: statin, insulin, microbiome, bacteroid, enterotypes
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse P Frumkin whose telephone number is (571)270-1849. The examiner can normally be reached Monday - Friday, 10-5 ET.
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/JESSE P FRUMKIN/ Primary Examiner, Art Unit 1685 July 10, 2026
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