DETAILED ACTION
The Applicant’s response, received 14 April 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 14 April 2025 has been entered.
Status of the Claims
Claims 21 and 23-31 are pending.
Claims 21 and 23-31 are rejected.
Claims 21 and 25 are objected to.
Priority
Claims 21, 23, 24, 27, 29, and 31 are given benefit to the claim for priority to Provisional Application No. 63/021,237, filed 07 May 2020.
Claims 25, 26, 28, and 30 are not given benefit to the claim for priority to Provisional Application No. 63/021,237, filed 07 May 2020, as noted below.
Claim 25 is not given the benefit of priority to Provisional Application No. 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 Provisional Application No. 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 Provisional Application No. 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 Provisional Application No. 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 Interpretation
The claim interpretations under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, in the Office action mailed 18 February 2025 are withdrawn in view of the amendment received 14 April 2025.
Claims 21 and 26 recite the term “user interface.” This term is interpreted to mean any device (e.g., mobile devices) with a central processing unit (CPU) that connects with input and output devices such as keyboards, mice, touchscreens, monitors, display, etc. (Specification, para. [00119]).
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 the active steps of performing the process of training the model. Therefore, claim 21, and those claims dependent therefrom, only require using an already trained machine learning model.
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 training the model with new training data.
Claim Objections
The objections to claims 21, 24, 25, 26, and 27 in the Office action mailed 18 February 2025 are withdrawn in view of the amendment received 14 April 2025.
The amendment received 14 April 2025 has been fully considered, however after further consideration, new grounds of objection are raised in view of the amendment.
Claim 21 is objected to because of the following informalities: The indentation of the claims 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…”). This objection can be remedied by indenting the limitations reciting the steps that are performed by the processor, and also by adding the word “and” between the “apply…” and “generate…” steps (i.e., before the penultimate step that is performed by the processor).
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 line 16.
Claim 25 is objected to because of the following informalities: The word “demonstrates” in line eight should be replaced with the word “demonstrate.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The rejection of claims 21, 22, 25, 26, and 28-31 under 35 U.S.C. 112(a) in the Office action mailed 18 February 2025 is withdrawn in view of the amendment received 14 April 2025.
The rejection of claims 21-31 under 35 U.S.C. 112(b) in the Office action mailed 18 February 2025 is withdrawn in view of the amendment received 14 April 2025.
The rejection of claim 22 under 35 U.S.C. 112(d) in the Office action mailed 18 February 2025 is withdrawn in view of the amendment received 14 April 2025.
The amendment received 14 April 2025 has been fully considered, however after further consideration new grounds of rejection are raised under 35 U.S.C. 112(b) in view of the amendment.
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. i
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.
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 24 recites the limitation “said 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 “clear and concise questions” because the terms “clear” and “concise” are relative terms that are 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 terms “clear and concise” with regard to “questions.”
Claim 26 is further 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, 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)).
Claim Rejections - 35 USC § 101
The rejection of claims 21 and 23-31 under 35 U.S.C. 101 in the Office action mailed 18 February 2025 is maintained with modification in view of the amendment received 14 April 2025.
The rejection of claim 22 is withdrawn in view of the cancellation of this claim in the amendment received 14 April 2025.
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).
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 (machine or 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 using a neural network architecture configured 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 diverse dataset of genetic and phenotypic information which enables said machine learning model to learn complex relationships that leads to accurate and reliable predictions (i.e., mathematical concepts).
Dependent claim 24 further recites:
wherein said personalized health report includes:
a detailed explanation of the 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 said individual's genetic profile (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 clear and concise questions (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 (i.e., mental processes).
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).
Dependent claim 29 further recites:
utilize user feedback for system optimization (i.e., 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).
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., training machine learning models; 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.
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;
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; a hardware processor; a computer memory coupled to said hardware processor; a computer storage device; a user interface; and a network interface; invokes 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, does not integrate the judicial exceptions into a practical application (see MPEP 2106.04(d)(1)).
The additional elements of receiving data; accessing a database to obtain data; storing data; transmitting data; and providing secure communications; are merely pre-solution or post-solution activities – nominal 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 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, 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 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; a hardware processor; a computer memory coupled to said hardware processor; a computer storage device; a user interface; a network interface; receiving data; accessing a database to obtain data; storing data; transmitting data; and using a neural network architecture; 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 transmitting encrypted data and providing secure communications (claim 29) are conventional. Evidence for the conventionality is shown by Jin et al. (IEEE Access, 2019, Vol. 7, pp. 61656-61669, newly cited). 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, 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 a 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 14 April 2025 have been fully considered, however they are not persuasive.
The Applicant states on page 10 (para. 8) of the Remarks that claim 21 has been amended to recite “wherein the machine learning model is trained using a neural network architecture configured to identify complex genotype-phenotype correlations through iterative optimization of network weights based on training data comprising known phenotype-genotype pairs, resulting in improved prediction accuracy compared to traditional statistical methods.” The Applicant further states on page 11 (para. 2) of the Remarks, that the amended claims are directed to patent eligible subject matter, and that the Federal Circuit has distinguished between claims that are directed to a judicial exception and those that are not, such as claims that improve the functioning of a computer or other technology or technological field (e.g., Diamond v. Diehr; Gottschalk v. Benson (summarizing Enfish); and McRO). The Applicant further states (para. 3) that the amended claims recite a specific implementation of machine learning technology that provides a technological improvement over conventional approaches to genotype prediction, and that the claims are not merely directed to an abstract idea of analyzing genetic data, but rather to a particular machine learning technique that enables more accurate genotype predictions.
These arguments are not persuasive, because first, the instant claimed method of using a trained machine learning model to predict genotype classifications comprises abstract ideas (e.g., mathematical concepts) that are identified at Step 2A Prong One in the above rejection and that are not integrated into a practical application at Step 2A Prong Two, as noted and discussed in the rejection above. Second, the abstract ideas alone cannot provide the improvement to a technology or technical field, but rather, the improvement can be found when considering the claim as a whole, i.e., the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception are evaluated together to determine whether the claim integrates the judicial exception into a practical application at 2A Prong Two, as noted in the rejection above, and thus, when the claims as a whole are considered, (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)). Third, when all additional elements in claims 21 and 23-31 are evaluated individually and in combination at Eligibility Step 2B, they are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exceptions (MPEP 2106.05(II)). Thus, the instant claimed specific implementation of machine learning technology is a purported improvement to the abstract idea (e.g., genotype prediction), and not an improvement to computer functionality itself, or an improvement to another technology or technical field.
The Applicant states on page 11 (para. 4) of the Remarks that the specification (e.g., at para. [00108]) supports that machine learning models can be trained using various techniques including neural networks and supervised or unsupervised training, and further states (para. 5) that any suitable machine learning technique may be used, such as linear regression, logistical regression, random forest, gradient boosted machine (GBM), and/or a deep neural network, and that a machine learning model may be initialized with no data, with random data, and/or with predetermined data such as the information for the first relational model. The Applicant further states (page 12, para. 1) that the amended claim specifies how the neural network architecture achieves improved accuracy through iterative optimization of network weights, training on known phenotype-genotype pairs, and identifying complex correlations that improve upon traditional statistical approaches, and that this represents a concrete improvement to computer technology because the neural network architecture enables identification of complex patterns that could not be detected through conventional statistical analysis. The Applicant further states that the iterative weight optimization process allows the model to continuously refine and improve its prediction accuracy, while the training on known pairs provides a specific technical mechanism for achieving the improved accuracy.
These arguments are not persuasive, because the limitations referenced in the argument comprise the limitations that are identified as abstract ideas at Step 2A Prong One in the above rejection, and as discussed in the foregoing response to arguments, the recited judicial exceptions are not integrated into a practical application of the exceptions when the claims as a whole are evaluated at Step 2A Prong Two (MPEP 2106.04(d)), and when all of the additional elements are evaluated individually and in combination at Eligibility Step 2B, they are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exceptions (MPEP 2106.05(II)). Thus, the instant claimed specific implementation of machine learning technology is a purported improvement to the abstract idea (e.g., genotype prediction), and not an improvement to computer functionality itself, or an improvement to another technology or technical field.
The Applicant states on page 12 (para. 2) of the Remarks that the amended claims move beyond generally linking the use of a judicial exception to a particular technological environment by reciting specific technical details about how the machine learning model is implemented and trained, and that the combination of elements imposes meaningful limits on any abstract idea by requiring a specific neural network architecture and training process, rather than claiming the concept of predicting genotypes at a high level of generality. The Applicant further states (para. 3) that the claimed invention, as amended, integrates any alleged abstract idea into a practical application by reciting specific improvements to machine learning technology for genotype prediction.
These arguments are not persuasive, because first, and as discussed in the above rejection, the additional element of using a neural network architecture provides nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)), and furthermore, merely confines the use of the abstract idea to the particular technological environment of neural networks (MPEP 2106.05(h)). Second, steps of training a machine learning model, e.g., a neural network, comprise mathematical calculations for optimizing the model, and therefore encompass mathematical concepts, and as discussed in the foregoing responses to arguments, the judicial exceptions alone cannot provide the improvement, i.e., the practical application (2A Prong Two) and/or inventive concept (2B).
Claim Rejections - 35 USC § 103
The Applicant’s amendment received 14 April 2025 has been fully considered, however after further consideration, new grounds of rejection are raised in view of the amendment.
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, newly cited) in view of Millican, III et al. (US 2016/0070881, newly cited).
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 controllers 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 are prima facie obvious. i
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; newly cited).
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.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
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If