DETAILED ACTION
The Applicant’s response, received 15 October 2025, 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 21 and 23-31 are pending.
Claims 21 and 23-31 are rejected.
Claims 21 and 24 are objected to.
Priority
This application claims benefit of 63/021,237 filed 07 May 2020.
Therefore, except as noted below, the effective filing date of the claimed invention is 07 May 2020.
The priority determinations set forth below are of record and reiterated herein.
Claims 25, 26, 28, and 30 are not given benefit to the claim for priority to 63/021,237, filed 07 May 2020, for the reasons provided below.
Claim 25 is not given the benefit of priority to 63/021,237, filed 07 May 2020, because there is not support for the limitations reciting “validated genotype data.”
Claim 26 is not given the benefit of priority to 63/021,237, filed 07 May 2020, because there is not support for the limitations reciting “validate data consistency,” and “provide user feedback during analysis to enhance analysis accuracy.”
Claim 28 is not given the benefit of priority to 63/021,237, filed 07 May 2020, because there is not support for the limitation reciting “provide statistical estimates of genotype classifications with associated confidence levels.”
Claim 30 is not given the benefit of priority to 63/021,237, filed 07 May 2020, because there is not support for the limitation reciting “analyze drug interaction risks.”
Therefore, the effective filing date of claims 25, 26, 28, and 30 is 30 April 2021; and the effective filing date of claims 21, 23, 24, 27, 29, and 31 is 07 May 2020.
Claim Objections
The objection to claim 25 in the Office action mailed 02 October 2025 has been withdrawn in view of the amendment received 15 October 2025.
The Applicant’s amendment received on 15 October 2025 has been fully considered, however after further consideration, the objections to claim 21 in the Office action mailed 02 October 2025 have been maintained in view of the amendment and are reiterated below.
Claim 21 is objected to because of the following informalities: The indentation of the claim limitations does not distinguish between the steps that the processor performs (i.e., “receive…”; “apply…” and “generate…”) and the components of the system (i.e., “a hardware processor…” and “a computer memory…”). The limitations reciting the steps that are performed by the processor should be indented such that there is a distinction between the components of the system and the processes performed by those components.
Claim 21 is objected to because of the following informalities: The word “the” should be inserted between the word “which” and the word “machine” in lines 13-14.
Appropriate correction is required.
The Applicant’s amendment received on 15 October 2025 has been fully considered, however after further consideration, new grounds of objection are raised in view of the amendment.
Claim 24 is objected to because of the following informalities: There should be a space between ‘said’ and ‘genetic’ at step (i).
Appropriate correction is required.
Response to Arguments
The Applicant’s amendment received 15 October 2025 has been fully considered but is not persuasive.
The Applicant states on page 2 of the Remarks the informalities have been addressed in the amended claims.
This argument/remark is not persuasive, to the extent that certain objections in the Office action mailed 02 October 2025 have been maintained because they were not addressed in the present amendment, as noted and discussed above.
Claim Rejections - 35 USC § 112
The amendment received 15 October 2025 has been fully considered, however after further consideration, new grounds of rejection are raised under 35 U.S.C. 112(a), first paragraph, in view of the amendment, as noted below.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21 and 23-31 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 21 recites the limitation “compared to traditional statistical methods” however, the specification does not contain the term “traditional” or the term “traditional statistic methods” with regard to statistically significant correlation, therefore the amendment appears to be a new scope not contemplated nor supported by the present specification.
Claims 23-31 are rejected for depending from claim 21 and failing to remedy the failure of claim 21 to comply with the written description requirement.
Claim 23 recites the limitation “a genetically and phenotypically diverse dataset, defined as a dataset comprising…” however, the specification does not contain the term “diverse” or the term “diverse dataset” with regard to genetic data, therefore the amendment appears to be a new scope not contemplated nor supported by the present specification.
Claim 24 recites the limitation “the individual's genetic profile, wherein ‘genetic profile’ refers to a graphical or tabular summary of said individual’s genotype classifications as determined by said machine learning model” however, the specification does not contain the term “genetic profile” or the term “tabular summary” with regard to an individual’s genetic data, therefore the amendment appears to be a new scope not contemplated nor supported by the present specification.
Claim 26 recites the limitation “questions that are presented in a manner that is unambiguous and concise, such that each question is phrased to minimize user confusion and elicit specific, relevant information” however, the specification does not contain the term “unambiguous” or the term “concise” with regard to prompting a user to provide data, therefore the amendment appears to be a new scope not contemplated nor supported by the present specification.
Claim 27 recites the limitation “wherein said measures include encryption, access controls, and audit logging” however, the specification does not contain the term “access controls” or the term “audit logging” with regard to protecting sensitive user information, therefore the amendment appears to be a new scope not contemplated nor supported by the present specification.
Claim 28 recites the limitation “wherein ‘confidence levels’ are numerical values representing said probability that a predicted genotype classification is correct, as determined by said model’s output” however, the specification does not contain the term “confidence levels” with regard to statistical estimates, therefore the amendment appears to be a new scope not contemplated nor supported by the present specification.
Claim 29 recites the limitation “wherein system optimization refers to said process of improving said accuracy, reliability, or efficiency of said genotype analysis system based on aggregated user feedback” however, the specification does not contain the term “optimization” or the term “system optimization” or the term “aggregated user feedback” with regard to a network interface, therefore the amendment appears to be a new scope not contemplated nor supported by the present specification.
Claim 31 recites the limitation “wherein ‘improves system reliability’ means that said report includes information and recommendations that are based on validated genotype predictions, thereby increasing said trustworthiness and repeatability of said system’s outputs” however, the specification does not contain the term “reliability” or the term “trustworthiness” or the term “repeatability” with regard to a detailed report, therefore the amendment appears to be a new scope not contemplated nor supported by the present specification.
The rejection of claim 24 under 35 U.S.C. 112(b), second paragraph, in the Office action mailed 02 October 2025 has been withdrawn in view of the amendment received 15 October 2025, as noted below.
Claim 24 previously recited the limitation “said individual’s genetic profile” at line six. The present amendment has deleted the word “said”.
The rejection of claim 26 under 35 U.S.C. 112(b), second paragraph, in the Office action mailed 02 October 2025 has been withdrawn in part in view of the amendment received 15 October 2025, to the extent noted below.
The rejection of claim 26 for reciting the limitation “clear and concise questions” has been withdrawn because the limitation “clear” has been deleted in the amendment.
The amendment received 15 October 2025 has been fully considered, however after further consideration, the rejection of claims 21 and 23-31 under 35 U.S.C. 112(b), second paragraph, in the Office action mailed 02 October 2025 have been maintained to the extent that they have been reiterated below.
The amendment received 15 October 2025 has been fully considered, however after further consideration, new grounds of rejection are raised under 35 U.S.C. 112(b), second paragraph, in view of the amendment, as noted below.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21 and 23-31 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The following rejections in the Office action mailed 02 October 2025 are reiterated below.
Claim 21 is indefinite for reciting the limitation “improved prediction accuracy compared to traditional statistical methods” because it is not clear as to what the metes and bounds are with respect to what is considered to be improved prediction accuracy and what is considered to be traditional statistical methods, or what it is compared to as improved.
Claims 23-31 are indefinite for depending from independent claim 21 and for failing to remedy the indefiniteness of claim 21.
Claim 23 is indefinite for reciting the relative term “diverse dataset.” This term is not defined by the claim, and the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention, and therefore it is not clear as to what the metes and bounds are for the term “diverse” with regard to “dataset.”
Claim 26 is indefinite for reciting “wherein said user interface is configured to…validate data consistency” because it is not clear as to whether the validation is performed by the hardware processor, and the user interface is merely displaying data.
Claim 26 is further indefinite for reciting the limitation “wherein said user interface is configured to” because the user interface is not part of the system of claim 21 (i.e., claim 21 only recites ‘receive’ data from a user interface), and therefore it is not clear as to whether this limitation is merely defining the process in which phenotypic data received from the user interface was previously generated, or alternatively, whether the user interface is intended to be part of the claimed system.
Claim 27 is indefinite for reciting “a computer storage device configured to…implement robust data privacy and security measures to protect sensitive user information” because it is not clear as to whether the storage device is merely to store data, and the implementation step is performed by the hardware processor.
Claim 28 is indefinite for reciting “learn and adapt to new data and insights” because it is not clear as to what differentiates new data from insights, since machine learning models are trained on data.
Claim 29 is indefinite for reciting “a network interface configured to…utilize user feedback for system optimization” because it is not clear as to whether the network interface is merely communicating data, and the system optimization is performed by the hardware processor. This claim is further indefinite because it is not clear as to what aspect of the system is subject to the step of optimization.
Claim 31 is indefinite for reciting the functional limitation “generate a detailed report that provides an analytical basis for genotype predictions and personalized health recommendations that improves system reliability” because it is not clear as to whether the step of generating a report is what is actually providing the improvement to system reliability, or alternatively, whether the report merely provides the data resulting from the claimed steps (e.g., genotype predictions) and the claimed improvement to system reliability is just an intended result of claimed steps leading to the generating of the detailed report. Therefore, the boundaries of the claim scope are unclear (MPEP 2173.05(g)).
The following rejections are newly raised in view of the present amendment.
Claim 24 recites the limitation “the individual’s genetic profile” at line six. There is insufficient antecedent basis for this limitation in the claim.
Claim 26 is indefinite for reciting the limitation “questions that are presented in a manner that is unambiguous and concise,” because the terms “unambiguous” and “concise” are relative terms, and the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention, and therefore it is not clear as to what the metes and bounds are for the terms “unambiguous and concise” with regard to “questions.”
Response to Arguments
The Applicant’s arguments/remarks received 15 October 2025 have been fully considered but are not persuasive.
The Applicant states on pages 2-3 of the Remarks that all claims now particularly point out and distinctly claim the subject matter regarded as the invention, as required by 35 U.S.C. 112(b).
The Applicant’s arguments/remarks are not persuasive, at least to the extent that certain rejections under 35 U.S.C. 112(b) in the Office action mailed 02 October 2025 have been maintained in the above rejection because issues noted in the rejection were not addressed in the present amendment, as noted and discussed above.
Claim Rejections - 35 USC § 101
The rejection of claims 21 and 23-31 under 35 U.S.C. 101 in the Office action mailed 02 October 2025 has been maintained with modification in view of the amendment received 15 October 2025, as noted below.
The rejection has been modified to incorporate newly amended claim limitations.
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 21 and 23-31 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) mental processes, i.e., concepts performed in the human mind (e.g., observation, evaluation, judgement, opinion); (b) mathematical concepts (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (c) a law of nature (e.g., naturally occurring relationships).
Claim Interpretations
Claim 21 recites the limitation “wherein said machine learning model is trained using a neural network architecture.” This limitation is interpreted as a product-by-process limitation, with the product being the trained machine learning model, and not requiring active steps of performing the process of training the model. Therefore, claim 21, and those claims dependent therefrom, only require using a machine learning model that has been previously trained.
Claim 25 recites the limitation “update said machine learning model with new data to improve future predictions.” This limitation is interpreted to comprise a subsequent step of updating the pre-trained model by training the model with new data, i.e., fine-tuning the model.
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 21 and 23-31 are directed to a computer-implemented system (i.e., a machine and/or a manufacture) for analyzing an individual’s genotype.
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 21 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
apply a machine learning model to the phenotypic data to predict genotype classifications (i.e., mental processes and mathematical concepts);
wherein said machine learning model is trained to identify statistically significant correlations between phenotype data and genotype classifications through iterative optimization of network weights based on training data comprising known phenotype-genotype pairs (i.e., mathematical concepts);
resulting in improved prediction accuracy compared to traditional statistical methods (i.e., mental processes); and
generate a personalized health report based on predicted genotype classifications (i.e., mental processes).
Independent claim 21, and those claims dependent therefrom, further recite a law of nature by associating an individual’s genomic data (e.g., genotype classification) with phenotypes (e.g., behavior (Specification, para. [0097])), i.e., a genotype-phenotype correlation (Specification, para. [0062]) (MPEP 2106.04(b)).
Dependent claims 23-31 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 23 further recites:
wherein said machine learning model is trained on a genetically and phenotypically diverse dataset (i.e., mathematical concepts),
defined as a dataset comprising genetic and phenotypic information from individuals representing multiple populations, age groups, sexes, and environmental backgrounds, such that said dataset enables said machine learning model to learn complex relationships and generalize predictions across a broad spectrum of individuals (i.e., mathematical concepts).
Dependent claim 24 further recites:
wherein said personalized health report includes:
a detailed explanation of said genetic risk factors identified (i.e., mental processes);
specific recommendations for preventive measures and/or lifestyle changes (i.e., mental processes);
information directed to potential drug responses and/or side effects (i.e., mental processes); and
a visual representation of the individual's genetic profile (i.e., mental processes);
wherein “genetic profile” refers to a graphical or tabular summary of said individual’s genotype classifications as determined by said machine learning model (i.e., mental processes).
Dependent claim 25 further recites:
compare predicted genotype classifications to validated genotype data to confirm accuracy (i.e., mental processes); and
update said machine learning model with new data to improve future predictions, and demonstrate a continuous improvement process (i.e., mental processes and mathematical concepts).
Dependent claim 26 further recites:
prompt a user to provide phenotypic data through a series of questions that are presented in a manner that is unambiguous and concise, such that each question is phrased to minimize user confusion and elicit specific, relevant information (i.e., mental processes);
validate data consistency (i.e., mental processes); and
provide user feedback during analysis to enhance analysis accuracy (i.e., mental processes).
Dependent claim 27 further recites:
implement robust data privacy and security measures to protect sensitive user information, wherein said measures include encryption, access controls, and audit logging (i.e., mental processes; and mathematical concepts).
Dependent claim 28 further recites:
wherein said machine learning model is configured to:
incorporate additional factors that include age, sex, family history, and environmental factors to refine genotype predictions (i.e., mathematical concepts);
continuously learn and adapt to new data and insights through a feedback loop (i.e., mathematical concepts); and
provide statistical estimates of genotype classifications with associated confidence levels (i.e., mathematical concepts),
wherein “confidence levels” are numerical values representing said probability that a predicted genotype classification is correct, as determined by said model’s output (i.e., mathematical concepts).
Dependent claim 29 further recites:
utilize user feedback for system optimization, wherein system optimization refers to said process of improving said accuracy, reliability, or efficiency of said genotype analysis system based on aggregated user feedback (i.e., mental processes; and mathematical concepts).
Dependent claim 30 further recites:
analyze drug interaction drug risks (i.e., mental processes);
provide personalized medication recommendations based on a genetic profile of said user (i.e., mental processes); and
provide medication risk assessments (i.e., mental processes).
Dependent claim 31 further recites:
generate a detailed report that provides an analytical basis for genotype predictions and personalized health recommendations that improves system reliability (i.e., mental processes),
wherein “improves system reliability” means that said report includes information and recommendations that are based on validated genotype predictions, thereby increasing said trustworthiness and repeatability of said system’s outputs (i.e., mental processes).
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., generate a personalized health report based on predicted genotype classifications; analyze drug interaction risks; provide personalized medication recommendations based on a genetic profile of said user; and provide medication risk assessments), 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., apply a machine learning model; and provide statistical estimates of genotype classifications with associated confidence levels) 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 21 and 23-31 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.
In the instant application, the claims provide additional elements to receive and store data and perform data analysis (e.g., computer hardware), however once the data is received, the subsequent steps only perform analysis and/or calculations using the data and a machine learning model to generate a report. Thus, the claims do not recite any limitations to which the generated report is practically applied.
Dependent claims 23, 24, 26, and 28 do not recite any elements in addition to the judicial exception and thus are part of the judicial exception.
The additional elements in independent claim 21 include:
a computer;
a hardware processor;
receive phenotypic data from a user interface (i.e., receive data);
using a neural network architecture;
a computer memory coupled to said hardware processor that allows storage of said machine learning model and at least one database of genetic information and phenotypic data from which machine learning model extracts data.
The additional elements in dependent claims 25, 26, 27, 29, 30, and 31 include:
hardware processor (claims 25, 30, and 31);
access an encrypted database of genetic information to obtain validated genotype data (claim 25);
a user interface (claim 26);
a computer storage device (claim 27);
store user-provided phenotypic data in a secure and encrypted format (claim 27);
store predicted genotype classifications and personalized health reports (claim 27);
a network interface (claim 29);
transmit encrypted health reports (claim 29); and
provide secure healthcare provider communications (claim 29).
The additional elements of a computer (claim 21); a hardware processor (claims 21, 25, 30, and 31); a computer memory coupled to said hardware processor (claim 21); a computer storage device (claim 27); a user interface (claim 26); and a network interface (claim 29); invoke a computer and/or computer-related components merely as tools for use in the claimed process, and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (see MPEP 2106.04(d)(1)).
The additional elements of receive data (claim 21); access a database to obtain data (claims 21 and 25); store data (claim 27); transmit encrypted data (claim 29); and provide secure communications (claim 29); 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)).
The additional element of using a neural network architecture (claim 21) provides nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)) and merely confines the use of the abstract idea to the particular technological environment of neural networks (MPEP 2106.05(h)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and/or do not amount to more than mere instructions to implement an abstract idea on a generic computer; and as such, when all limitations in claims 21 and 23-31 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 21 and 23-31 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 23, 24, 26, and 28 do not further recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claim 21 and dependent claims 25, 26, 27, 29, 30, and 31 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 computer (claim 21); a hardware processor (claims 21, 25, 30, and 31); a computer memory coupled to said hardware processor (claim 21); a computer storage device (claim 27); a user interface (claim 26); a network interface (claim 29); receive data (claim 21); access a database to obtain data (claims 21 and 25); store data (claim 27); transmit data (claim 29); and using a neural network architecture (claim 21); are conventional (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional elements of transmit encrypted data (claim 29) and provide secure communications (claim 29) are conventional. Evidence of conventionality is shown by:
Jin et al. (IEEE Access, 2019, Vol. 7, pp. 61656-61669, as cited in the Office action mailed 02 October 2025).
Jin et al. reviews secure and privacy-preserving medical data sharing with a focus on block-chain approaches (Title; and Abstract) and shows privacy protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) that was enacted to strengthen medical data governance (page 61656, col. 1, para. 2) through technical safeguard requirements such as access control standards (e.g., encryption and decryption) and transmission security (e.g., integrity controls and encryption) (Table 1).
Therefore, when taken alone (i.e., individually), all additional elements in claims 21 and 23-31 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 21 and 23-31 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 15 October 2025 have been fully considered but are not persuasive.
The Applicant states on page 4 (Section 1.) of the Remarks that the claims are not directed to an abstract idea or law of nature, and further states that the claims are directed to a specific, practical, and technological solution: a computer-implemented system that uses a neural network-based machine learning model to analyze genotype and phenotype data, generate personalized health reports, and continuously improve prediction accuracy. The Applicant further states that the claimed invention is not merely an abstract idea or law of nature, but a concrete application of machine learning technology to solve a technical problem in the field of genomics. The Applicant further states that the claims recite specific technical elements: a hardware processor, computer memory, encrypted database, user interface, network interface, and a computer storage device. The Applicant further states that the claims specify a particular neural network architecture, iterative optimization of network weights, and training on known phenotype-genotype pairs, and further states that the claims require the generation of personalized health reports, secure data storage and transmission, and continuous model improvement based on validated data and user feedback.
These arguments/remarks are not persuasive, because first, Step 2A is a two-prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. Together, these prongs represent the first part of the Alice/Mayo test, which determines whether a claim is directed to a judicial exception. As noted and discussed in the above rejection, at least one abstract idea and a law of nature were identified at Step 2A Prong One. Second, at Step 2A Prong Two, evaluating whether judicial exceptions are integrated into a practical application comprises: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I at 2106.04(d) of the MPEP, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h). As noted in the rejection above, when all limitations in claims 21 and 23-31 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), they are deemed to not recite any additional elements that would integrate a judicial exception into a practical application (MPEP 2106.04(d)). That is, the claims provide additional elements to receive and store data and perform data analysis (e.g., computer hardware), however once the data is received, the subsequent steps only perform analysis and/or calculations using the data and a machine learning model to generate a report. Thus, the claims do not recite any limitations to which the generated report is practically applied.
The Applicant states on page 4 (Section 2.) of the Remarks that the claims are integrated into a practical application, and further states that the claimed system applies machine learning to genotype analysis in a manner that is rooted in computer technology and provides a practical application: the neural network architecture is specifically configured to identify statistically significant correlations between phenotype data and genotype classifications, which cannot be performed in the human mind or with pen and paper. The Applicant further states on page 5 of the Remarks that the system generates personalized health reports that include detailed explanations, recommendations, drug response information, and visual representations, all based on validated genotype predictions. The Applicant further states that the system implements robust data privacy and security measures, including encryption, access controls, and audit logging, which are technical solutions to real-world problems in handling sensitive genetic data. The Applicant further states that the system continuously learns and adapts to new data and insights, improving prediction accuracy and reliability over time. The Applicant further states that these elements go beyond merely invoking a computer as a tool; they represent a specific improvement to the technological process of genotype analysis and health report generation.
These arguments/remarks are not persuasive, because first, the limitation that recites using neural network architecture is not identified as a judicial exception from the mental process grouping of abstract ideas. Second, as noted in the foregoing response to arguments, evaluating whether judicial exceptions are integrated into a practical application comprises: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I at 2106.04(d) of the MPEP, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h). As noted in the rejection above, when all limitations in claims 21 and 23-31 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), they are deemed to not recite any additional elements that would integrate a judicial exception into a practical application (MPEP 2106.04(d)). That is, the claims provide additional elements to receive and store data and perform data analysis (e.g., computer hardware), however once the data is received, the subsequent steps only perform analysis and/or calculations using the data and a machine learning model to generate a report. Thus, the claims do not recite any limitations to which the generated report is practically applied (i.e., there is no practical application of the recited judicial exceptions). Third, the argued limitations including “identify statistically significant correlations between phenotype data and genotype classifications” “generates personalized health reports that include detailed explanations, recommendations, drug response information, and visual representations, all based on validated genotype predictions” “robust data privacy and security measures, including encryption, access controls, and audit logging” and “continuously learns and adapts to new data” are limitations that are identified as judicial exceptions at Step 2A Prong One, and as previously noted and discussed, the claims do not recite any additional elements to which the judicial exceptions are practically applied.
The Applicant states on page 5 (Section 3.) of the Remarks that the claims recite an inventive concept, and further states that the combination of elements in the claims is not well-understood, routine, or conventional. The Applicant further states that the use of a neural network architecture trained on diverse, validated datasets to predict genotype classifications and generate personalized health reports is not conventional in the field, as supported by the specification at paragraphs [00108], [0062], & [0097]. The Applicant further states that the system’s ability to continuously improve prediction accuracy through iterative optimization and feedback is a technical advance over traditional statistical methods, and further states that the implementation of secure data storage, transmission, and privacy measures is a technical solution to a problem of handling sensitive genetic information. The Applicant further states that the claims, when considered as a whole, provide significantly more than the judicial exception and transform the abstract idea into a patent-eligible application.
These arguments/remarks are not persuasive, because first, a conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, which comprises steps of: (1) carrying over the identification of any additional element(s) in the claim from Step 2A Prong Two; (2) carrying over the conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h); (3) re-evaluating any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), 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 (4) evaluating whether any additional element 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). As noted in the rejection above, when all additional elements (i.e., those limitations identified at Step 2A Prong Two) in claims 21 and 23-31 have been evaluated individually and in an ordered combination at Eligibility Step 2B, they are deemed to not contribute an inventive concept, i.e., do not amount to significantly more than the judicial exceptions (MPEP 2106.05(II)). Second, the instant claimed technical solution to a problem of handling sensitive genetic information is a purported improvement to the abstract idea (i.e., data analysis), and not an improvement to computer functionality itself, or an improvement to another technology or technical field.
The Applicant states on page 6 (Section 4.) of the Remarks that controlling case law supports eligibility, and points to Enfish and states that claims directed to a specific improvement in computer functionality are patent-eligible, and here, the instant claims recite a specific neural network architecture and training process that improves the accuracy and reliability of genotype predictions, representing a technological improvement. The Applicant further points to McRO and states that claims that recite a specific set of rules for automating a process are patent-eligible, and further states that the present claims recite specific steps for training and applying a neural network to genotype analysis, not merely a result. The Applicant further points to DDR Holdings, and states that claims that solve a problem specifically arising in the realm of computer technology are patent-eligible, and further states that the present invention solves the technical problem of accurate genotype prediction and secure health data management using machine learning. Finally, the Applicant states that the Examiner’s reliance on generic computer implementation is misplaced, as the claims recite specific technical solutions and improvements.
These arguments/remarks 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 recite a computer-implemented system for analyzing an individual’s genotype and generating a personalized health report based on predicted genotype classifications, whereas in contrast, the improvement recited in Enfish is found in a data structure (i.e., a data structure as a programmatic mechanism used within computer memory to store and manipulate data efficiently) 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 at analogizing the instant claims with the eligibility determination in McRO, it is noted that in McRO, when looked at as a whole, claim 1 is directed to a patentable, technological improvement over the existing, manual 3-D animation techniques, i.e., the claim recited “a specific asserted improvement in computer animation” that was directed to the creation of something physical – namely, the display of lip synchronization and facial expressions of animated characters on screens for viewing by human eyes (i.e., automatically animating characters using the particular rules), and therefore was determined to not be directed to an unpatentable abstract idea at Eligibility Step 2A (i.e., Alice step one). Unlike the technological improvement found in McRO, the instant claimed technical solution to a problem of handling sensitive genetic information is a purported improvement to the abstract idea (i.e., data analysis), and not an improvement to computer functionality itself, or an improvement to another technology or technical field. Third, regarding the Applicant’s attempt at analogizing the instant claims to DDR Holdings, the instant claims are not analogous to the claims in DDR Holdings, because the instant claims recite a computer-implemented system for analyzing an individual’s genotype and generating a personalized health report based on predicted genotype classifications, whereas in contrast, the improvement recited in DDR Holdings is found when the limitations of the patent’s asserted claims are taken together as an ordered combination, and determined to recite additional elements that are not routine or conventional, i.e., the claims recite an invention that is not merely the routine or conventional use of the Internet. Fourth, regarding the argument that the Examiner’s reliance on generic computer implementation is misplaced, it is noted that the claims merely recite using conventional computer components and/or processes to perform analysis of genomic data to generate a personalized health report.
The Applicant states on page 6 (Section 5.) of the Remarks that according to MPEP 2106.04(d), a claim is not directed to a judicial exception if it integrates the exception into a practical application. The Applicant further states that the present claims do so by reciting specific technical elements and improvements. The Applicant further states that MPEP 2106.05(a)-(h) further supports eligibility where claims improve computer functionality or another technology or technical field, as is the case here. The Applicant further states on page 7 of the Remarks that the claims do not merely invoke generic computer components, but recite a specific neural network architecture, training process, and secure data management, all of which are improvements to the technical field of genotype analysis.
These arguments/remarks are not persuasive, because the claims do not recite any additional elements that either apply, rely on, or use the judicial exceptions in a manner that imposes a meaningful limit on the judicial exceptions, and therefore any purported improvement is in the judicial exception(s) themselves. Stated a different way, the claims use generic computer components (e.g., the limitation “a hardware processor” is generic to any particularly recited processor (i.e., species)) to analyze genomic data to identify statistically significant correlations between phenotype data and genotype classifications and then generate a personalized health report based on predicted genotype classifications. Thus, the purported improvements to the technical field of genotype analysis are purported improvements the abstract idea of data analysis.
Claim Rejections - 35 USC § 103
The Applicant’s amendment received 15 October 2025 has been fully considered, however after further consideration, the rejections under 35 U.S.C. 103 in the Office action mailed 02 October 2025 have been maintained in view of the amendment, as noted below.
The rejection of claims 21, 23-25, and 27-31 under 35 U.S.C. 103 as being unpatentable over Trunck et al. (US 2019/0019083, newly cited) in view of Millican, III et al. in the Office action mailed 02 October 2025 has been maintained in view of the amendment received 15 October 2025.
The rejection of claim 26 under 35 U.S.C. 103 as being unpatentable over Trunck et al. in view of Millican, III et al. as applied to claims 21, 23-25, and 27-31 above, and further in view of Knoop et al. in the Office action mailed 02 October 2025 has been maintained in view of the amendment received 15 October 2025.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21, 23-25, and 27-31 are rejected under 35 U.S.C. 103 as being unpatentable over Trunck et al. (US 2019/0019083, as cited in the Office action mailed 02 October 2025) in view of Millican, III et al. (US 2016/0070881, as cited in the Office action mailed 02 October 2025).
Independent claim 21 encompasses a computer-implemented system for applying a machine learning model to phenotypic data to predict genotype classifications and then generating a personalized health report based on predicted genotype classifications.
Dependent claims 23-25 and 27-31 further define characteristics of the machine learning model and characteristics of the personalized health report and characteristics of the computer-implemented system.
Trunck et al. teaches making predictive assignments that relate to genetic information and are based on machine learning techniques, and explicitly show utilizing machine learning models (e.g., neural networks) that have access to records describing genomic data for individuals, and that also have access to records describing characteristics for individuals, and using these records, the models may predictively assign characteristics to individuals based on known genetic variants within those individuals, or to predictively assign genetic variants to individuals based on known characteristics of those individuals.
Millican, III et al. teaches a system, method and graphical user interface for creating modular, patient transportable genomic analytic data.
Regarding claim 21, Trunck et al. shows systems and methods for performing predictive assignments pertaining to genetic information, i.e., predicting characteristics based on genetic variants (paras. [0037] – [0060]), where a controller selects one or more machine learning models, and for each individual in the records, predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual, and then the controller generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display (Abstract; and FIG. 2). Trunck et al. further shows a hardware processor and a computer readable storage medium (paras. [0101] – [0103]); receiving one or more characteristics of an individual as input, and using this data to predictively assign one or more genetic variants of the individual (paras. [0032] & [0056]; and FIG. 1); a reverse process for predicting genetic variants based on characteristics, i.e., phenotypes (paras. [0076] – [0083]; and FIG. 7); and training and using neural networks that facilitate predictive assignments (paras. [0061] – [0075]; and FIGS. 5-6).
Regarding claim 21, Trunck et al. does not show generating a personalized health report based on predicted genotype classifications.
Regarding claim 21, Millican, III et al. shows systems and methods for the generation, online viewing and display of reports created by the analysis of DNA, mRNA, and protein, that include the severity, diagnosis and prognosis of the specimen, and include visual and textual analysis, prognostic and treatment information, and comprehensive patient genotype result and drug recommendation by specialty (Abstract; and FIGS. 6-10).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Trunck et al. by incorporating methods for generating a personalized health report based on genotype and/or phenotype classifications as shown by Millican, III et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Trunck et al. with the methods of Millican, III et al., because Millican, III et al. shows the generation of a comprehensive personalized health report for a patient that is based on the analysis and diagnosis of the patient’s genomic sample (e.g., DNA). This modification would have had a reasonable expectation of success given that both Trunck et al. and Millican, III et al. disclose providing reports that summarize the results of genomic analyses.
Regarding claim 23, Trunck et al. further shows the machine learning models have been trained using training data sets that indicate known characteristics and known genetic variants of a specific population (para. [0042]) and that genomics data and characteristics data are aggregated over time for multiple individuals, and may be utilized as training data sets (para. [0057]).
Regarding claim 24, Trunck et al. further shows generating a report, e.g., based on the report, a user schedules an additional genetic test to check for a SNP, and takes the grandparent to a follow-up medical visit to test for shellfish allergies, and the results indicate that the grandparent does not have the SNP but does have a shellfish allergy, i.e., the report indicates a preventive measure and/or a lifestyle change (para. [0100]).
Regarding claim 24, Trunck et al. does not show (i) a detailed explanation of the genetic risk factors identified; (iii) information directed to potential drug responses and/or side effects; and (iv) a visual representation of said individual's genetic profile.
Regarding claim 24, Millican, III et al. further shows a clinical report that includes a) genotype and phenotype data; b) comprehensive and customized diagnostic specific drug recommendations; c) drug recommendations for the current medications patient is taking; d) drug to drug, food to drug, alcohol to drug interactions; and e) all relevant lab test results (para. [0031]).
Therefore, it would have been further obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Trunck et al. by incorporating methods for generating a personalized health report based on genotype and/or phenotype classifications as shown by Millican, III et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Trunck et al. with the methods of Millican, III et al., because Millican, III et al. shows the generation of a comprehensive personalized health report for a patient that is based on the analysis and diagnosis of the patient’s genomic sample (e.g., DNA). This modification would have had a reasonable expectation of success given that both Trunck et al. and Millican, III et al. disclose providing reports that summarize the results of genomic analyses.
Regarding claim 25, Trunck et al. further shows machine learning models trained based on a vetted set of training data ([0095]); and analyzing input data indicating accuracy of a predictively assigned genetic variant, determining a score for a machine learning model based on the input via a cost function, and revising the machine learning model based on the score (claim 3).
Regarding claim 25, Trunck et al. does not show accessing an encrypted database of genetic information to obtain validated genotype data.
Regarding claim 25, Millican, III et al. further shows data is transferred to a secured data storage ([0020]); and using a virtual private network for communicating between devices and systems (para. 0021]).
Therefore, it would have been further obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Trunck et al. by incorporating methods for secured data transmission and/or storage as shown by Millican, III et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Trunck et al. with the methods of Millican, III et al., because Millican, III et al. shows using secure channels for transmitting sensitive and/or private data (e.g., para. [0085]). This modification would have had a reasonable expectation of success given that both Trunck et al. and Millican, III et al. disclose providing reports that summarize the results of an individual’s genomic analysis.
Regarding claim 27, Trunck et al. further shows storage of genomics and characteristics data (paras. [0028] & [0029]).
Regarding claim 27, Trunck et al. does not show storing user-provided phenotypic data in a secure and encrypted format; storing predicted genotype classifications and personalized health reports; and implementing robust data privacy and security measures to protect sensitive user information.
Regarding claim 27, Millican, III et al. further shows data is transferred to a secured data storage ([0020]); using a virtual private network for communicating between devices and systems (para. [0021]); and reports are accessible via secure channel by physician (para. [0085] & FIG. 4).
Therefore, it would have been further obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Trunck et al. by incorporating methods for secured data storage as shown by Millican, III et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Trunck et al. with the methods of Millican, III et al., because Millican, III et al. shows transferring sequencing data to a secured data storage (e.g., para. [0020]). This modification would have had a reasonable expectation of success given that both Trunck et al. and Millican, III et al. disclose storing data of an individual’s genomic analysis.
Regarding claim 28, Trunck et al. further shows characteristics, i.e., phenotypes, can include a history of medical treatment for the individual (which would include, e.g., age, sex, family history, etc.) (para. [0029]) and new genomics data and characteristics data are aggregated over time and used to train and/or revise one or more machine learning models (paras. [0056] & [0057]); input/feedback indicating whether the predictively assigned characteristics are valid, or are inaccurate, is received, and based on this feedback, the model is analyzed using a cost function, and in this manner, the machine learning models adaptively increase in accuracy and precision over time (para. [0047]); and compares the confidence values against the confidence thresholds (para. [0100]).
Regarding claim 29, Trunck et al. further shows network adapter interfaces (para. [0104]); and feedback and optimization (para. [0047]).
Regarding claim 29, Trunck et al. does not show transmitting encrypted health reports; or providing secure healthcare provider communications.
Regarding claim 29, Millican, III et al. further shows using a virtual private network for communicating between devices and systems (para. [0021]); and reports are accessible via secure channel by physician (para. [0085] & FIG. 4).
Therefore, it would have been further obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Trunck et al. by incorporating methods for securely transmitting health data and providing secure communication with healthcare providers as shown by Millican, III et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Trunck et al. with the methods of Millican, III et al., because Millican, III et al. shows using secure channels for transmitting sensitive and/or private data (e.g., para. [0085]). This modification would have had a reasonable expectation of success given that both Trunck et al. and Millican, III et al. disclose providing reports that summarize the results of an individual’s genomic analysis.
Regarding claim 30, Trunck et al. does not show methods to analyze drug interaction risks; provide personalized medication recommendations based on a genetic profile of said user; and provide medication risk assessments.
Regarding claim 30, Millican, III et al. further shows outputting drug, food, and alcohol interactions for current medications (para. [0082]); a current medications recommendation list (para. [0077]); and drug interactions (para. [0079]).
Therefore, it would have been further obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Trunck et al. by incorporating methods for analyzing drug interactions and providing personalized medication recommendations and medication risk assessments as shown by Millican, III et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Trunck et al. with the methods of Millican, III et al., because Millican, III et al. shows the generation of a comprehensive personalized health report for a patient that is based on the analysis and diagnosis of the patient’s genomic sample (e.g., DNA). This modification would have had a reasonable expectation of success given that both Trunck et al. and Millican, III et al. disclose providing reports that summarize the results of genomic analyses.
Regarding claim 31, Trunck et al. further shows that reports may also be utilized to develop applications pertaining to the genetic prediction server and/or for internal research (para. [0035]).
Therefore, claims 21, 23-25, and 27-31 would have been prima facie obvious.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Trunck et al. in view of Millican, III et al. as applied to claims 21, 23-25, and 27-31 above, and further in view of Knoop et al. (US 2019/0096509, as cited in the Office action mailed 02 October 2025).
Dependent claim 26 further defines the configuration of said user interface.
Knoop et al. teaches a mechanism to implement a health risk assessment system for adaptively and dynamically generating a personalized questionnaire for health risk assessment of a patient.
Regarding claim 26, Trunck et al. in view of Millican, III et al. as applied to claims 21, 23-25, and 27-31 above, do not show said user interface is configured to prompt a user to provide phenotypic data through a series of clear and concise questions; validate data consistency; and provide user feedback during analysis to enhance analysis accuracy.
Regarding claim 26, Knoop et al. shows a mechanism to implement a health risk assessment system for adaptively and dynamically generating a personalized questionnaire for health risk assessment of a patient (Abstract); and further shows that when a patient begins a questionnaire for a health risk assessment, the mechanisms of the questionnaire prompts the patient with basic questions and or information, such as, for example, age, height, weight, race, sex, or the like (para. [0028]); and further shows aspects of the mechanism that provide for maximizing the accuracy of the risk prediction, e.g., questions selection criteria and priority based on the relative importance of a question in maximizing predictive power, and also, the more questions that the patient answers, the lower the uncertainty (paras. [0021] – [0025]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Trunck et al. in view of Millican, III et al. as applied to claims 21, 23-25, and 27-31 above, by incorporating a mechanism to implement a health risk assessment system for adaptively and dynamically generating a personalized questionnaire for health risk assessment of a patient, as shown by Knoop et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the method of Trunck et al. in view of Millican, III et al. with the method of Knoop et al., because Knoop et al. shows a technical framework to adaptively and dynamically tailor a health-risk questionnaire with predictive analytic algorithms to offer a shortest, most relevant, and intuitive set of questions to a patient, which allows an accurate assessment of health risk to the patient (e.g., para. [0100]). This modification would have had a reasonable expectation of success given that both Trunck et al. in view of Millican, III et al. and Knoop et al. disclose methods for analyzing a patient and/or user’s health related information for generating actionable recommendations.
Response to Arguments
The Applicant’s arguments/remarks received 15 October 2025 have been fully considered but are not persuasive.
The Applicant states on page 7 (D. Obviousness) that regarding claim 21, neither Trunck et al. nor Millican, III et al. teaches or suggests a system comprising a neural network architecture specifically configured to identify statistically significant correlations between phenotype data and genotype classifications through iterative optimization, resulting in improved prediction accuracy compared to traditional statistical methods. The Applicant further states that the cited references do not disclose or suggest the specific combination of elements recited in claim 21, nor do they provide the technical improvements described in the specification. Regarding the dependent claims, the Applicant further states that the cited references do not disclose or suggest the specific limitations of the dependent claims, including training on a genetically and phenotypically diverse dataset (claim 23), generating reports with detailed explanations and visual representations (claim24), secure and encrypted data storage and transmission (claims 25, 27, 29), continuous model improvement (claim 25), and robust privacy and security measures (claim 27). Regarding motivation to combine, the Applicant further states that the Examiner’s rationale for combining the references is based on generalizations and does not provide a sufficient articulated reasoning with rational underpinning, as required by KSR, and further states that the combination would not yield the claimed invention, nor would it provide the specific technical improvements recited. Regarding secondary considerations, the Applicant further states that the claimed invention provides unexpected results and technical improvements, including increased prediction accuracy, reliability, and security, which are not taught or suggested by the prior art.
These arguments/remarks are not persuasive, because first, the above rejections provide a claim-by-claim analysis as to how each claim limitation reads on the cited references and thus show that the cited references as mapped to the claim limitations teach or render obvious the instant claims. Second, regarding motivation to combine the references used in the above rejections, each of the motivation statements in the above rejections provide a reasoned, explicit explanation of why a person of ordinary skill in the art would have been motivated to combine the references. Third, regarding secondary considerations and unexpected results, it is not clear from the Applicant’s arguments as to what the unexpected result is from applying a machine learning model to genomic data to generate a personalized health report, and regarding a purported technical improvement, it is further not clear from the Applicant’s arguments as to what the technical improvement is in view of the Trunck et al. reference explicitly teaching making predictive assignments that relate to genetic information and are based on machine learning techniques, and explicitly show utilizing machine learning models (e.g., neural networks) that have access to records describing genomic data for individuals, and that also have access to records describing characteristics for individuals, and using these records, the models may predictively assign characteristics to individuals based on known genetic variants within those individuals, or to predictively assign genetic variants to individuals based on known characteristics of those individuals.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687
Prosecution Insights