DETAILED ACTION
The Applicant’s response, received 11 March 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim 1 is objected to.
Priority
There are no domestic or foreign applications for which benefit is claimed.
The effective filing date of the claimed invention is 29 March 2021.
Claim Interpretation
The claim interpretations in the Office action mailed 11 September 2025 are maintained in view of the amendment and arguments/remarks received 11 March 2026.
Claims 1, 2, 3, 11, 12, and 13 recite the term “biological indicator.” This term is interpreted to mean an element of data associated with an individual’s biological system that denotes a health status, and may include an element that denotes an individual’s genetic composition (Specification, para. [0010]). The limitation of a “biological indicator” is interpreted to recite a product-by-process limitation of obtaining genomic data without an active step of performing genome sequencing.
Claims 1, 6, 7, 11, 16, and 17 recite the term “mitochondrial profile.” This term is interpreted to mean information as a profile and/or estimation of an individual’s mitochondrial health status, e.g., a profile and/or estimation may denote that an individual’s mitochondrial function is outputting lower than normal ATP quantities (Specification, para. [0013]).
Claims 1, 3, 4, 6-11, 13, 14, and 16-20 recite the term “as a function of.” This term is interpreted to be read as “based on,” and therefore could comprise a mathematical concept (e.g., “y is a function of x” (denoted y = y(x)) meaning that y varies according to whatever values x takes on) or alternatively could not comprise a mathematical concept.
Claims 1, 7, 8, 11, 17, and 18 recite the term “probabilistic vector.” This term is interpreted to mean a data structure that represents one or more quantitative values and/or measures the probability associated with a mitochondrial function modification (Specification, para. [0013]).
Claims 1, 7, 11, and 17 recite the term “medical examination.” This term is interpreted to mean an evaluation of the individual’s health system and may include one or more examinations that identifies, characterizes, and/or otherwise evaluates the DNA responsible for mitochondrial function, e.g., southern blot examination, polymerase chain reaction, genetic sequencing examination, and mass spectrometry examination (Specification, para. [0015]). The limitation of a “medical examination” is interpreted to not recite an active step of performing a medical examination, and is limited to receiving and interpreting genomic data.
Claims 7 and 17 recite the term “follow-up recommendation.” This term is interpreted to mean a recommendation for an individual to receive a secondary and/or subsequent analysis to confirm, monitor, and/or evaluate the first medical examination results, and may include a nuclear DNA test, mitochondrial DNA test, and the like (Specification, para. [0027]). The limitation of a “follow-up recommendation” is interpreted to mean a data output generated by the model.
Claims 7 and 17 recite the term “second medical examination.” This term is interpreted to mean a secondary and/or subsequent analysis to confirm, monitor, and/or evaluate the first medical examination results, and may include one or more examinations that identifies, characterizes, and/or otherwise evaluates the DNA responsible for mitochondrial function, e.g., southern blot examination, polymerase chain reaction, genetic sequencing examination, and mass spectrometry examination (Specification, para. [0027]). The limitation of a “second medical examination” is interpreted to not recite an active step of performing a medical examination, and is limited to receiving and interpreting a second set of genomic data.
Claims 1 and 11 recite the term “medical guideline.” This term is interpreted to mean a recommendation and/or guideline for mitochondrial functioning, e.g., a mitochondrion should produce 100 – 150 mol/L of ATP per day (Specification, para. [0028]).
Claims 1, 9, 11, and 19 recite the term “biological modification.” This term is interpreted to mean an effect that a mitochondrial profile has on the health system of an individual, e.g., one or more physical symptoms such as seizures, cancer, and thyroid dysfunction (Specification, para. [0013]).
Claims 1, 7, 11, and 17 recite the term “profile machine-learning model.” This term is interpreted to mean a machine-learning model to produce a mitochondrial profile output given probabilistic vectors and biological indicators as inputs (Specification, para. [0017]). The limitation of a “profile machine-learning model” is interpreted to not recite an active step of training the model.
Claims 1 and 11 recite the term “biological machine-learning model.” This term is interpreted to mean a machine-learning model to produce a biological modification output given medical guidelines and/or mitochondrial profiles as inputs (Specification, para. [0029]). The limitation of a “biological machine-learning model” is interpreted to not recite an active step of training the model.
Claims 9 and 19 recite the term “edible machine-learning model.” This term is interpreted to mean a machine-learning model to produce an edible output given nourishment compositions and nourishment demands as inputs (Specification, para. [0034]). The limitation of an “edible machine-learning model” is interpreted to not recite an active step of training the model.
Claims 1, 10, 11, and 20 recite the term “nourishment machine-learning model.” This term is interpreted to mean a machine-learning model to produce a nourishment program output given edibles and/or mitochondrial outcomes as inputs (Specification, para. [0039]). The limitation of an “nourishment machine-learning model” is interpreted to not recite an active step of training the model.
Claims 1, 9, 11, and 19 recite the term “edible.” This term is interpreted to mean a source of nourishment that may be consumed by a user such that the user may absorb the nutrients from the source, e.g., legumes, plants, fungi, and dumplings (Specification, para. [0032]).
Claims 1, 2, 11, and 12 recite the term “inheritance element.” This term is interpreted to mean an element associated with the inherited DNA from one or more parents of an individual, e.g., a particular region of an individual’s DNA that was inherited from a mother (Specification, para. [0010]).
Claims 4 and 14 recite the term “spontaneity element.” This term is interpreted to mean an element of date relating to the amount of spontaneity that a mutation may exhibit, e.g., expansion of the CGG triplet in the FMR-1 gene occurs in 1 of 1500 males and 1 of 2500 females (Specification, para. [0011]).
Claims 8 and 18 recite the term “deoxyribonucleic acid vector.” This term is interpreted to mean a measurable value associated with the deoxyribonucleic acid of an individual (Specification, para. [0032]). The limitation of a “deoxyribonucleic acid vector” is interpreted to recite a product-by-process limitation of obtaining or receiving genomic data without an active step of performing genome sequencing.
Claims 9 and 19 recite the term “nourishment demand.” This term is interpreted to mean a requirement and/or necessary amount of nutrients required for a user to consume (Specification, para. [0033]).
Claims 1, 10, 11, and 20 recite the term “mitochondrial outcome.” This term is interpreted to mean an outcome that an edible may generate according to a predicted and/or purposeful plan (Specification, para. [0038]). This limitation is further interpreted to mean receiving and interpreting genomic data.
Claims 1 and 11 recite the limitation “treatment outcome.” This limitation is interpreted to not recite an active step of performing a treatment, and is limited to receiving and interpreting genomic data of an intended outcome that an edible may generate according to a predicted and/or purposeful plan (Specification, para. [0038]).
Response to Arguments
The Applicant’s arguments/remarks received 11 March 2026 have been fully considered, and are persuasive.
The Applicant states on page 1 (para. 4) that the Applicant generally agrees that the cited terms should be interpreted consistent with their plain and ordinary meaning in view of the specification, and notes (para. 5) that the cited passages in the specification provide illustrative examples and explanatory context and should not be interpreted as limiting the scope of the claims unless expressly recited in the claim language. The Applicant further states (para. 6) that where claims expressly recite training or retraining operations for machine-learning models, the claims should be interpreted as including those steps, and further states on page 2 (para. 1) that accordingly, that examination proceed based on the claim language as presently amended.
These arguments/remarks are persuasive, because under the broadest reasonable interpretation (BRI) standard, the foregoing claim limitations are given their plain and ordinary meaning consistent with how a person skilled in the art would understand them in light of the specification.
Claim Objections
The objections to claims 1 and 11 in the Office action mailed 11 September 2025 are withdrawn in view of the amendment received 11 March 2026.
The amendment received 11 March 2026 has been fully considered, however after further consideration, new grounds of objection are raised in view of the amendment.
Claim 1 is objected to because of the following informalities: The last two clauses in the amended claim (i.e., “generating…” and “nourishment machine-learning…”) should be a single clause.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The rejection of claims 1-20 under 35 U.S.C. 112(b) in the Office action mailed 11 September 2025 has been withdrawn in view of the amendment received 11 March 2026.
Claim Rejections - 35 USC § 101
The Applicant’s amendment received 11 March 2026 has been fully considered, however after further consideration, the rejection of claims 1-20 under 35 U.S.C. 101 in the Office action mailed 11 September 2025 is maintained with modification in view of the amendment.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a law of nature without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion); and (c) a law of nature (naturally occurring relationship).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-10 are directed to a computer system (i.e., a machine or a manufacture) for generating a mitochondrial dysfunction nourishment program; and claims 11-20 are directed to a method (i.e., a process) for generating a mitochondrial dysfunction nourishment program.
Therefore, the claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
produce a mitochondrial profile as a function of the biological indicator, wherein the mitochondrial profile indicates a mitochondrial dysfunction and is associated with an inheritance element (i.e., mental processes and mathematical concepts), and wherein producing the mitochondrial profile further comprises:
identifying a probabilistic vector as a function of a medical examination (i.e., mental processes and mathematical concepts); and
producing the mitochondrial profile as a function of the probabilistic vector and the biological indicator using a profile machine-learning model (i.e., mental processes and mathematical concepts); which comprises:
filtering the profile training data set to sub-categories of probabilistic vectors comprising a probability as a function of the inheritance element (i.e., mental processes and mathematical concepts);
selecting at least one filtered profile training data set of the plurality of profile training data sets as a function of a group using the profile machine-learning model (i.e., mental processes and mathematical concepts);
training the profile machine-learning model as a function of the at least one filtered profile training data set (i.e., mental processes and mathematical concepts); and
replacing the profile machine-learning model with an updated machine-learning model, wherein the updated machine-learning model comprises at least a software update, the software update comprising a new nourishment metric that relates to a modified nourishment vector (i.e., mental processes);
identify a biological modification as a function of the mitochondrial profile (i.e., mental processes and mathematical concepts), wherein identifying the biological modification further comprises:
identifying the biological modification as a function of the medical guideline and mitochondrial profile using a biological machine-learning model (i.e., mental processes and mathematical concepts); wherein using the biological machine-learning model comprises:
training the biological machine-learning model with a biological training set (i.e., mental processes and mathematical concepts), wherein the biological training set comprises at least a medical guideline and mitochondrial profile correlated to at least a biological modification relating to central nervous system dysfunction (i.e., mental processes and mathematical concepts); wherein training the biological machine-learning model comprises:
updating the biological training set as a function of a previously generated correlation of a medical guideline and the mitochondrial profile to the biological modification of a previous iteration of the biological machine-learning model (i.e., mental processes and mathematical concepts);
retrain the biological machine-learning model using the updated biological training set (i.e., mental processes and mathematical concepts); and
identifying the biological modification as a function of the trained biological machine-learning model (i.e., mental processes and mathematical concepts);
determine an edible as a function of the biological modification (i.e., mental processes and mathematical concepts); and
generate a nourishment program as a function of the edible (i.e., mental processes and mathematical concepts), wherein generating the nourishment program further comprises:
training a nourishment machine-learning model using the mitochondrial outcome as input to generate a nourishment program, wherein the nourishment machine-learning model uses the mitochondrial outcomes as an input and outputs a nourishment program (i.e., mental processes and mathematical concepts);
updating the mitochondrial outcome based on a modified edible (i.e., mental processes);
retraining the nourishment machine-learning model based on updated mitochondrial outcome (i.e., mental processes and mathematical concepts); and
generating an updated nourishment program as a function of the retrained nourishment machine-learning model and the updated mitochondrial outcome (i.e., mental processes and mathematical concepts).
Independent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
producing a mitochondrial profile as a function of the biological indicator, wherein the mitochondrial profile indicates a mitochondrial dysfunction and is associated with an inheritance element (i.e., mental processes and mathematical concepts), and wherein producing the mitochondrial profile further comprises:
identifying a probabilistic vector as a function of a medical examination (i.e., mental processes and mathematical concepts);
producing the mitochondrial profile as a function of the probabilistic vector and the biological indicator using a profile machine-learning model (i.e., mental processes and mathematical concepts); which comprises
filtering the profile training data set to sub-categories of probabilistic vectors comprising a probability as a function of the inheritance element (i.e., mental processes and mathematical concepts);
selecting at least one filtered profile training data set of the plurality of profile training data sets as a function of a group using the profile machine-learning model (i.e., mental processes and mathematical concepts);
training the profile machine-learning model as a function of the at least one filtered profile training data set; and
replacing the profile machine-learning model with an updated machine-learning model, wherein the updated machine-learning model comprises at least a software update, the software update comprising a new nourishment metric that relates to a modified nourishment vector (i.e., mental processes);
identifying a biological modification as a function of the mitochondrial profile (i.e., mental processes and mathematical concepts), wherein identifying the biological modification further comprises:
identifying the biological modification as a function of the medical guideline and mitochondrial profile using a biological machine-learning model (i.e., mental processes and mathematical concepts); wherein using the biological machine-learning model comprises:
training the biological machine-learning model with a biological training set (i.e., mental processes and mathematical concepts), wherein the biological training set comprises at least a medical guideline and mitochondrial profile correlated to at least a biological modification relating to central nervous system dysfunction (i.e., mental processes and mathematical concepts); wherein training the biological machine-learning model comprises:
updating the biological training set as a function of a previously generated correlation of a medical guideline and the mitochondrial profile to the biological modification of a previous iteration of the biological machine-learning model (i.e., mental processes and mathematical concepts);
retrain the biological machine-learning model using the updated biological training set (i.e., mental processes and mathematical concepts); and
identifying the biological modification as a function of the trained biological machine-learning model (i.e., mental processes and mathematical concepts);
determining an edible as a function of the biological modification (i.e., mental processes and mathematical concepts); and
generating a nourishment program as a function of the edible (i.e., mental processes and mathematical concepts), wherein generating the nourishment program further comprises:
training a nourishment machine-learning model using the mitochondrial outcome as an input to generate a nourishment program, wherein the nourishment machine-learning model uses the mitochondrial outcome as an input and outputs a nourishment program (i.e., mental processes and mathematical concepts);
updating the mitochondrial outcome based on a modified edible (i.e., mental processes);
retraining the nourishment machine-learning model based on the updated mitochondrial outcome (i.e., mental processes and mathematical concepts); and
generating an updated nourishment program as a function of the retrained nourishment machine-learning model and the updated mitochondrial outcome (i.e., mental processes and mathematical concepts).
Independent claims 1 and 11 recite a law of nature by associating an individual’s genomic data (biological indicator) with a phenotype (mitochondrial dysfunction), i.e., a genotype-phenotype correlation (MPEP 2106.04(b)).
Dependent claims 2 and 12 further recite:
wherein the biological indicator includes an inheritance element (i.e., mental processes).
Dependent claims 3 and 13 further recite:
identifying a mutation component and obtaining the biological indicator as a function of the mutation component (i.e., mental processes and mathematical concepts).
Dependent claims 4 and 14 further recite:
identifying a spontaneity element (i.e., mental processes);
determining a mutation rate as a function of the spontaneity element and a mutation grouping (i.e., mental processes and mathematical concepts); and
identifying the mutation component as a function of the mutation rate (i.e., mental processes and mathematical concepts).
Dependent claims 5 and 15 further recite:
wherein the mutation component includes an epigenetic element (i.e., mental processes).
Dependent claims 6 and 16 further recite:
wherein producing the mitochondrial profile further comprises determining a mitochondrial dysfunction and producing the mitochondrial profile as a function of the mitochondrial dysfunction (i.e., mental processes and mathematical concepts).
Dependent claims 7 and 17 further recite:
identifying a first probabilistic vector as a function of a first medical examination (i.e., mental processes and mathematical concepts);
generating a second probabilistic vector as a function of the second medical examination (i.e., mental processes and mathematical concepts); and
producing the mitochondrial profile as a function of the first probabilistic vector and the second probabilistic vector using the profile machine-learning model (i.e., mental processes and mathematical concepts).
Dependent claims 8 and 18 further recite:
identifying the probabilistic vector as a function of the mitochondrial deoxyribonucleic acid vector and the nuclear deoxyribonucleic acid vector (i.e., mental processes and mathematical concepts).
Dependent claims 9 and 19 further recite:
producing a nourishment demand as a function of the biological modification (i.e., mental processes and mathematical concepts); and
determining the edible as a function of the nourishment composition and the nourishment demand using an edible machine-learning model (i.e., mental processes and mathematical concepts).
Dependent claims 10 and 20 further recite:
generating the nourishment program as a function of the mitochondrial outcome using a nourishment machine-learning model (i.e., mental processes and mathematical concepts).
Dependent claims 3 and 13 further recite a law of nature by correlating an individual’s genomic data (biological indicator) with a phenotype (genomic mutations), i.e., genotype-phenotype correlation (MPEP 2106.04(b)).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., filtering the profile training data set to sub-categories of probabilistic vectors comprising a probability as a function of the inheritance element; and selecting at least one filtered profile training data set), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., vectors (specification at para. [0014]); machine-learning processes such as simple linear regression (specification at para. [0024]); and algorithms that include probabilistic vectors as inputs and scoring functions that maximize the probability that a given input is associated with a given output (specification, para. [0051])) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Furthermore, a law of nature correlating a genotype-phenotype association is identified at Eligibility Step 2A Prong One.
Therefore, claims 1-20 recite an abstract idea and a law of nature.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 2-6 and 12-16 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claims 1 and 11 include:
a computing device (claims 1 and 11);
obtaining a biological indicator (i.e., obtaining data) (claims 1 and 11);
receiving the profile machine-learning model from a remote device (i.e., receiving data) (claims 1 and 11);
receiving a profile training data set, wherein the profile training data set correlates a plurality probabilistic vector data to a plurality of mitochondrial profile data (i.e., receiving data) (claims 1 and 11);
receiving a medical guideline (i.e., receiving data) (claims 1 and 11);
receiving the biological machine-learning model from a remote device (i.e., receiving data) (claims 1 and 11);
receiving a mitochondrial outcome, wherein the mitochondrial outcome is a treatment outcome (i.e., receiving data) (claims 1 and 11); and
the nourishment program is transmitted to the computing device (i.e., transmitting data) (claims 1 and 11).
The additional elements in dependent claims 7-10 and 17-20 include:
receiving a second medical examination as a function of a follow-up recommendation (i.e., receiving data) (claims 7 and 17);
obtaining a mitochondrial deoxyribonucleic acid vector (i.e., obtaining data) (claims 8 and 18);
receiving a nuclear deoxyribonucleic acid vector (i.e., receiving data) (claims 8 and 18);
receiving a nourishment composition from an edible directory (i.e., receiving data) (claims 9 and 19); and
receiving a mitochondrial outcome (i.e., receiving data) (claims 10 and 20).
The additional element of a computing device (claims 1 and 11) invokes a computer merely as a tool for use in the claimed process, i.e., performing the functions of receiving and/or outputting data and/or using a trained machine learning model and/or training a machine learning model, such that it amounts to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore is not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, does not integrate the judicial exceptions into a practical application (see MPEP 2106.04(d)(1)).
The additional elements of obtaining data (claims 1, 8, 11 and 18); receiving data (claims 1, 7, 8, 9, 10, 11, 17 and 20); receiving data from a remote device (claims 1 and 11); and transmitting data to the computing device (claims 1 and 11); are merely pre-solution or post-solution activities – nominal or tangential additions to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution activities, and as such, when all limitations in claims 1-20 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-6 and 12-16 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1 and 11 and dependent claims 7-10 and 17-20 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a computing device (claims 1 and 11); obtaining data (claims 1, 8, 11 and 18); receiving data (claims 1, 7, 8, 9, 10, 11, 17 and 20); receiving data from a remote device (claims 1 and 11); and transmitting data to the computing device (claims 1 and 11); are conventional (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
Therefore, when taken alone, all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Response to Arguments
The Applicant’s arguments/remarks received 03 April 2025 have been fully considered, but are not persuasive.
The Applicant summarizes the January 2019 Guidance on page 3 (para. 5) of the Remarks and summarizes the July 2024 Subject Matter Eligibility Examples (para. 6), and provides amended claim 1 (pages 4-5) and states on page 5 that according to MPEP 2106.04, independent claims 1 and 11 and their dependent claims 2-10 and 12-20, recite allowable subject matter under Step 2A and/or 2B of the eligibility analysis.
These arguments are not persuasive, because of the reasons given at Steps 2A and 2B in the above rejection.
The Applicant summarizes aspects of the MPEP at § 2106.04 (II)(A)(1) on page 5 (bottom) of the Remarks and states on page 6 (para. 2) that under Step 2A Prong One the Examiner alleges that claim 1 recites abstract ideas, specifically mental processes and mathematical concepts, and further states that the Applicant traverses this rejection. The Applicant summarizes aspects of the eligibility analysis at § 2106.04(a)(2)(III) of the MPEP with regard to mental processes (para. 3) and further states (para. 4) that claim 1 includes at least several limitations which cannot be practically performed in the human mind, e.g., training a profile machine-learning model using filtered profile training data sets and correlating a plurality of probabilistic vector data with mitochondrial profile data. The Applicant further states that such steps require computational processing of large, structured datasets, including filtering training datasets, performing probabilistic vector correlations across multiple biological indicators, and iteratively training a machine-learning model based on those correlations. The Applicant further states on page 7 (para. 1) of the Remarks that these operations involve algorithmic data processing and model training that cannot reasonably be performed mentally or with pen and paper, and accordingly, the claimed steps far exceed the scope of mental processes and require computer implementation to achieve a technical solution.
These arguments are not persuasive, because first, at Eligibility Step 2A Prong One, examiners evaluate whether the claim recites a judicial exception (emphasis added), i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. As noted in the above rejection, independent claims 1 and 11 are identified as reciting at least one judicial exception (e.g., identifying a probabilistic vector as a function of a medical examination (i.e., mental processes and mathematical concepts)), which requires that the claim(s) be further evaluated at Step 2A Prong Two. Second, with regard to the August 4, 2025 Memorandum (regarding reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101), the Memorandum clearly states (page 1) that it is not intended to announce any new USPTO practice or procedure and is meant to be consistent with existing USPTO guidance, and further states that Examiners should consult the specific MPEP sections for more thorough information. Third, regarding the Applicant’s argument that the claims recite steps that “require computational processing of large, structured data sets…,” it is noted that the amount of data and/or the amount of time to perform the process steps, in and of themselves is not a limitation which takes a process out of the realm of the human mind. It is the process performed on that data which is the mental step, and mental steps identified in the claims do not have to be the fastest, most efficient, or require specialized computing elements. Thus, although the amount of data may be considered to be significantly large and take considerable time and effort to process manually, the use of a computer to perform the claimed method at a rate and accuracy that can far outstrip the mental performance of a skilled artisan does not change the nature of the activity being performed (i.e., an abstract idea), and therefore does not materially alter the patent eligibility of the claimed subject matter.
The Applicant summarizes aspects of the eligibility analysis at §2106.04(a)(2)(I) of the MPEP with regard to mathematical concepts on page 7 (para. 2) of the Remarks and further summarizes (para. 3) aspects of the August 4, 2025 Memorandum (Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101) and states that in the Memorandum, the USPTO drew a distinction between Examples 39 and 47 of the Subject Matter Eligibility Examples, further stating that Example 39 illustrates claim limitations that merely involve an abstract idea, whereas Example 47 shows limitations that recite an abstract idea. The Applicant further states that the recitation of training the neural network in a first stage using the first training set in the Example 39 does not recite a judicial exception, regardless of whether it may involve or rely upon mathematical concepts. The Applicant further states (para. 4) that this can be distinguished from Example 47, which the USPTO characterizes as reciting an abstract idea, specifically, training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm. The Applicant further states on page 8 (para. 1) of the Remarks that the USPTO Memorandum distinguishes this claim in Example 47 from the claim in Example 39 on the basis that claim 2 of Example 47 requires specific mathematical calculations by referring to the mathematical calculations by name, i.e., a backpropagation algorithm and a gradient descent algorithm, and therefore recites a judicial exception, namely an abstract idea. The Applicant further states (para. 2) that claim 1, as amended, does not recite any specific mathematical calculations or named mathematical algorithms, and rather, the claim merely recites the use of machine-learning training as part of a technological process for generating a mitochondrial profile based on biological indicators and probabilistic vector data. The Applicant further states that the claim also does not recite a law of nature or natural phenomenon, but instead recites a system that processes biological indicator data using trained machine-learning models to generate a mitochondrial profile and a corresponding nourishment program, and therefore, accordingly, similar to Example 39, the claim limitations at most involve mathematical concepts as part of a broader technical process, but they do not recite a judicial exception.
These arguments are not persuasive, because first, as noted in the rejection above, independent claims 1 and 11 recite a law of nature by associating an individual’s genomic data (biological indicator) with a phenotype (mitochondrial dysfunction), i.e., a genotype-phenotype correlation (MPEP 2106.04(b)), and dependent claims 3 and 13 further recite a law of nature by correlating an individual’s genomic data (biological indicator) with a phenotype (genomic mutations), i.e., genotype-phenotype correlation (MPEP 2106.04(b)). Second, with regard to the Applicant’s attempt to analogize the instant claims to Example 39 and differentiate the instant claims from Example 47, it is noted that the proper legal basis for construing the scope of the claims is not by analogizing them to USPTO training examples, but by interpreting the plain meaning of the claim language in light of the specification. As noted at Eligibility Step 2A Prong One in the rejection above, by the plain meaning of the claim terms, read in light of their corresponding descriptions in the specification, the claims recite numerous mathematical concepts and mental processes capable of being performed in the human mind with the aid of a pencil and paper. Third, the Eligibility Examples are hypothetical and only intended to be illustrative of the claim analysis performed using MPEP 2106, and of the particular issues noted in each Example, and therefore, the Examples should be interpreted based on the fact patterns set forth in a particular Example, as other fact patterns (e.g., the instant claims) may have different eligibility outcomes. Fourth, with regard to whether a claim limitation recites a judicial exception or merely involves a judicial exception, it is noted that at Eligibility Step 2A: Prong One, examiners evaluate whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement." Fifth, it is important to note that a mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula or equation (MPEP 2106.04(a)(2)(I)), and therefore, a claim that recites a mathematical concept, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. As noted in the rejection above, when instant claims 1 and 11 are evaluated at Eligibility Step 2A: Prong One, the claims are determined to describe (i.e., recite) judicial exceptions from the mathematical concepts grouping of abstract ideas, as opposed to merely being based on or involving a mathematical concept.
The Applicant summarizes aspects of the eligibility analysis under Step 2A Prong Two on page 8 (para. 3) of the Remarks and states on page 9 (para. 2) that amended claim 1 recites limitations that apply computational modeling and machine learning techniques to biological indicator data to generate a mitochondrial profile, thereby improving technological processes for analyzing biological data and producing medically relevant profiles. The Applicant further states that similar to Enfish, the present claims recite a specific machine learning based framework for correlating probabilistic vector data with mitochondrial profile data and generating a mitochondrial profile using trained models, and that these limitations define a particular technological process for analyzing biological datasets and producing mitochondrial profiles, and accordingly, the claims integrate any alleged abstract idea into a practical application. The Applicant further points (para. 3) to Ex parte Desjardins and states (para. 4) that in Desjardins, the USPTO Appeals Review Panel (ARP) held claims directed to training a machine learning model subject matter eligible under Step 2A Prong Two. The Applicant further provides a summarization of aspects of Desjardins (Remarks, page 9, bottom, and page 10, top), and further states on page 10 that as in Desjardins, the present application represents an improvement in technology and is subject matter eligible under Step 2A Prong Two, and further states that just as Desjardins’ specification taught improvements to AI, the present application’s specification teaches improvements to AI, e.g., paras. [0019] & [0030]. The Applicant provides particular limitations of the instant claims on page 11 (bottom) and page 12 (top) and further states on page 11 (bottom) that similarly, like Desjardins, the claims of the present application reflect the AI improvements. The Applicant further states on page 12 (para. 2) that accordingly, claim 1 as amended recites patentable subject matter.
These arguments are not persuasive, because first, regarding the Applicant’s attempt at analogizing the instant claims to Enfish, the instant claims are not analogous to the claims in Enfish, because the instant claims broadly recite steps of using machine learning models to classify data, whereas in contrast, the improvement recited in Enfish is found in a data structure that corresponds to a storage and retrieval structure configured in a computer memory comprising a self-referential table that is designed to improve the way a computer stores and retrieves data in memory, and thus is an improvement to computer functionality itself. Stated a different way, the improvement was found in the structure of the table itself (e.g., relationships between rows and columns) as arranged (i.e., configured) in a physical memory device, irrespective of any particular data being stored or searched. Second, with regard to the Applicant’s attempt to analogize the instant claims to the claims in Desjardins, these arguments are not persuasive at least because the fact patterns differ between the claims at issue in Desjardins and the instant claims, not least in that the “ARP” in Desjardins notes that the Federal Circuit held that the eligibility determination should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea” (citing Enfish), prior to the “ARP” finding that the improvement to how the machine learning model operates allows artificial intelligence (AI) systems to use less of their storage capacity and enables reduced system complexity, as supported by the Specification – i.e., the improvement to the model provided an improvement to computer functionality itself. This fact pattern contrasts with the fact pattern of the instant claims, which broadly recite steps of receiving machine learning models, iteratively training the models, and using the models to classify data. Therefore, claims should be interpreted based on the fact patterns set forth in a particular claim, as other claims with different fact patterns may have different eligibility outcomes, as evidenced in the rejection of the instant claims above.
The Applicant states on page 12 (bottom) of the Remarks that the 2B analysis by the Office is moot in light of the amendments to claim 1 and the above arguments, and further states on page 13 (top) that the Office’s conclusion at Step 2B is not supported by the record or consistent with the applicable legal framework. The Applicant summarizes aspects of eligibility analysis under Step 2B (Remarks, page 13, para. 2) and further states that the MPEP guidance specifies how an examiner is permitted to conclude something is well-understood, routine, and conventional. The Applicant further states (para. 3) that amended claim 1 recites generating a mitochondrial profile using a profile machine-learning model trained on filtered profile training data sets and correlating probabilistic vector data with mitochondrial profile data. The Applicant further provides the Applicant’s own determination of which limitations constitute additional elements on pages 13-15 of the Remarks, and further states on page 15 (para. 2) that these additional elements are neither insignificant extra-solution activity nor merely token post-solution steps, and that similar to Bascom, the present claims recite a specific arrangement of machine learning operations that filter training datasets, correlate probabilistic vector data with mitochondrial profile data, and train a profile machine-learning model to generate mitochondrial profiles. The Applicant further states that this ordered combination of steps defines a technological process for processing complex biological datasets and generating mitochondrial profiles, rather than merely invoking a computer to perform generic functions, and therefore, accordingly, the amended claims, viewed as a whole, amount to significantly more than the purported abstract idea and satisfy the requirements of 35 U.S.C. 101. The Applicant further states that claim 11 recites, substantially, the same limitations as claim 1, and therefore the rejection of claim 11 has been overcome for the same reasons as to claim 1.
These arguments are not persuasive, because first, the Applicant’s argument incorrectly identifies multiple limitations as additional elements (e.g., limitations including the steps of “identifying a probabilistic vector as a function of a medical examination,” “filtering the profile training data set to sub-categories of probabilistic vectors comprising a probability as a function of the inheritance element,” and “selecting at least one filtered profile training data set of the plurality of profile training data sets as a function of a group using the profile machine-learning model,”) that are actually limitations that are part of the judicial exceptions identified at Step 2A Prong One, and accordingly, are not carried over to Step 2B for further analysis. Second, regarding the Applicant’s attempt at analogizing the instant claims to Bascom, it is noted that the fact pattern differs between the instant claims and the claims in Bascom, not least in that the claims in Bascom recited limitations that were conventional additional elements, but that were found to provide an inventive concept in their non-conventional and non-generic arrangement. In contrast to the fact pattern of Bascom, the fact pattern of the instant claims provide the additional elements of a computing device (claims 1 and 11); obtaining data (claims 1, 8, 11 and 18); receiving data (claims 1, 7, 8, 9, 10, 11, 17 and 20); receiving data from a remote device (claims 1 and 11); and transmitting data to the computing device (claims 1 and 11); which, as noted in the above rejection are conventional additional elements (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes), and even when evaluated as an ordered combination, these additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
Conclusion
No claims are allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.W.B./Examiner, Art Unit
/Joseph Woitach/Primary Examiner, Art Unit 1687
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