DETAILED ACTION
Applicant’s response, filed 10 July 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 .
Claim Status
Claims 1, 3-11, and 13-22 are pending and examined herein.
Claims 1, 3-11, and 13-22 are rejected.
Claim Interpretation
Claims 9, 17, and 20 recite “responsive to determining that the investigation need indicator satisfies the investigation need threshold condition, performing a predictive correlation analysis on the one or more related predictive input features to determine a related subset of the reduced-dimensionality feature set” which is interpreted to be a contingent clause (see MPEP 2111.04(II) which highlights the differences in interpretation between method claims and machine claims when a contingent clause is present).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-11, and 13-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1, 3-10, and 21 fall under the statutory category of a process and claims 11, 13-20, and 22 fall under the statutory category of a machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claims 1, 11, and 18 recite mental processes of “reducing a plurality of predictive input features of a genomic predictive domain each associated with a single nucleotide polymorphism (SNP) of a plurality of SNPs, to a per- marker proximate subset comprising only those predictive input features of the plurality of predictive input features having one or more of…”, “generating, based at least in part on the per-marker feature, a per-marker correlation value in relation to the target feature…”, “determining, based at least in part on the per-feature correlation value for each predictive input feature in the per-marker proximate subset, a per- marker feature for the predictive marker data object…”, “generating, based at least in part on the per-marker feature, a per-marker correlation value in relation to the target feature…”, “determining a reduced-dimensionality feature set from the plurality of predictive input features…”, “inputting, to a machine learning model configured to perform the predictive inference, the reduced-dimensionality feature set…”, “obtaining, from the machine learning model, one or more predictions associated with the target feature…”, and “initiating performance of one or more prediction-based actions based at least in part on the one or more predictions”.
Independent claims 1, 11, and 18 recite mathematical concepts of “generating, based at least in part on the per-marker feature, a per-marker correlation value in relation to the target feature…”, “determining, based at least in part on the per-feature correlation value for each predictive input feature in the per-marker proximate subset, a per- marker feature for the predictive marker data object…”, “generating, based at least in part on the per-marker feature, a per-marker correlation value in relation to the target feature…”, “inputting, to a machine learning model configured to perform the predictive inference, the reduced-dimensionality feature set”, and “obtaining, from the machine learning model, one or more predictions associated with the target feature”.
Dependent claim 4 and 14 further recite a mental process and mathematical concept of “determining a feature value…”, “determining an association value…”, and “determining the per-feature correlation value…”. Dependent claim 9, 17, and 20 recite a mental process of “determining an investigation need indicator…”, “determining whether the investigation need indicator satisfies an investigation need threshold condition”, “performing a predictive correlation analysis on the one or more related predictive input features…”. Dependent claims 9, 17, and 20 recite a mathematical concept of “determining an investigation need indicator for the predictive marker data object…”. Dependent claims 21 and 22 recite a mental process of “generating or modifying a prescription for a patient associated with the one or more predictions”.
The claims require a process of reducing the dimensionality of input features to a per-marker proximate subset based on the input features meeting criteria, generating a per-feature correlation value between input features in the per-marker proximate subset and a target feature, determining a per marker feature for the predictive marker data object based the per-feature correlation value for each predictive input feature in the subset, generating, for the per-marker feature, a per-marker correlation value in relation to the target feature, determining a reduced-dimensionality feature set based on the per-marker correlation value of the per-marker feature exceeding the per-feature correlation value for each predictive input feature in the subset, inputting the reduced-dimensionality feature set into a machine learning model (which can be a Bayesian network), obtaining from the model a prediction associated with the target feature, and initiating the performance of one prediction based actions based at least in part on the one predictions. The BRI of the claims encompasses organizing data (grouping data based on criteria), analyzing data (through correlation values, inputting data into a model, and obtaining a prediction), and making a judgment (through initiating performance of prediction-based actions). The human mind is capable of grouping data objects into a subset based on criteria, calculating correlation values, making a judgment based on correlation values, inputting features into a model (which can be a Bayesian network) to obtain a prediction, and initiating the performance of one or more prediction-based actions (for example adding information to patient records as contemplated in [101] of the instant specification). The claims recite mathematical calculations of generating correlation values, determining a per-marker feature for the predictive marker data object, inputting the reduced-dimensionality feature set in a machine learning model to obtain one or more predictions associated with the target feature. The steps of generating a per-feature correlation value and generating a per-marker correlation value encompass calculating a Pearson correlation coefficient (as shown in [082] and [091] of the instant disclosure) which is a mathematical calculation. The step of determining a per-marker feature for the predictive marker data object which encompasses generating a mean or sum of correlation values (as shown in [087] of the instant disclosure) which are mathematical calculations. Further, the steps inputting the reduced-dimensionality feature set in a machine learning model and obtaining one or more predictions associated with the target feature encompasses using a Bayesian network model (as shown in [100] of the instant disclosure) which is a series of mathematical calculations on numeric data to obtain a numeric output. Dependent claims 3, 5-8, 10, 13, 15, 16, 19 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept. Thus, claims 1, 3-11, and 13-22 recite judicial exceptions that are abstract ideas.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional elements in claims 1, 11, and 18 of using a generic computer and a non-transitory computer readable medium to cause a generic computer to perform judicial exceptions does not integrate the judicial exceptions into a practical application because this is simply applying the judicial exception to a generic computer without an improvement to computer technology.
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1, 3-11, and 13-22 are directed to the abstract idea.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional elements in claims 1, 11, and 18 of using a generic computer and a non-transitory computer readable medium to cause a generic computer to perform judicial exceptions is conventional as shown by MPEP 2106.05(b) and MPEP 2106.05(d)(II).
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
Response to Arguments
Applicant's arguments filed 10 July 2025 have been fully considered but they are not persuasive.
Applicant argues the human mind is not equipped to perform the steps of the claim because they are not observations, evaluations, judgments, or opinions. It is infeasible for the human mind to perform these operations and, thus, they cannot be practically performed mentally. For instance, as known to one of ordinary skill in the art, inputting a reduced-dimensionality feature set to a machine learning model requires the utilization of data transmission protocols to enable machine communication, thereby enabling the machine learning model to receive the subject reduced-dimensionality feature set. Further, initiating the performance of one or more prediction-based actions based at least in part on the one or more predictions cannot be practically performed mentally, as the human mind cannot initiate a prediction-based action.” (Reply p. 15).
This argument has been fully considered but found to be not persuasive. As described above, the BRI of the claims encompasses organizing data (grouping data based on criteria), analyzing data (through correlation values, inputting data into a model, and obtaining a prediction), and making a judgment (through initiating performance of prediction-based actions). The human mind is capable of performing these steps. As described in the instant disclosure the model may be a Bayesian Network which represents the probabilistic relationship between an event that occurred and the contributing factors of that event. The use of a Bayesian network has nodes that are each associated with a probability function that takes a particular set of values for the node’s parent variables as input and outputs a probability. The human mind is capable of using a probability function that intake a set of values to output a probability. Further, the human mind is able to perform prediction-based actions because it encompasses making a judgement. As shown in dependent claims 21 and 22 the prediction-based action may be generating or modifying a prescription which the human mind is capable of doing. One could identify relevant information and make a judgment on a prescription (either generate a prescription or modify an existing prescription). Thus, the human mind is capable of performing the identified mental processes in Step 2A Prong 1.
Applicant argues while the claimed predictions may involve mathematical concepts, they do not recite a mathematical concept. Moreover, the 2024 Guidance underscores that a "claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping) if it is only based on or involves a mathematical concept." 2024 Guidance. The 2024 Guidance provides an example of "claims that do not recite an abstract idea ( e.g., a mathematical concept) or other judicial exception," referring to XY, LLC v. Trans Ova Genetics, 968 F.3d 1323, 1330-32 (Fed. Cir. 2020). Id. There, the Federal Circuit determined that claims to a method of operating a flow cytometry apparatus to classify and sort particles into at least two populations in real time to more accurately classify similar particles was not directed to "the abstract idea of using a 'mathematical equation that permits rotating multi-dimensional data'" even though they may have involved mathematical concepts. Id. Here, as in XY, LLC v. Trans Ova Genetics, though the claims may involve "mathematical concepts," the claims are not directed to a mathematical concept. Therefore, claim 1 does not recite a mathematical concept as defined by the MPEP as clarified by the 2024 Guidance. (Reply p. 17).
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.04(a)(2)(I)(C) that “There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation”. The steps of generating a per-feature correlation value and generating a per-marker correlation value encompass calculating a Pearson correlation coefficient (as shown in [082] and [091] of the instant disclosure) which is a mathematical calculation. The step of determining a per-marker feature for the predictive marker data object which encompasses generating a mean or sum of correlation values (as shown in [087] of the instant disclosure) which are mathematical calculations. Further, the steps inputting the reduced-dimensionality feature set in a machine learning model and obtaining one or more predictions associated with the target feature encompasses using a Bayesian network model (as shown in [100] of the instant disclosure) which is a series of mathematical calculations on numeric data to obtain a numeric output. These steps recite mathematical calculations because the claims encompass embodiments in which calculating numeric values using functions or a mathematical models achieve these steps in the method. Therefore, the claims recite mathematical concepts because they recite steps of performing mathematical calculations. Further, XY, LLC v. Trans Ova Genetics has a different fact pattern than the instant case. The claims in XY, LLC v. Trans Ova Genetics were found to be patent eligible because the judicial exception provided an improvement in operating a flow cytometry apparatus (i.e., the judicial exception (the mathematical concept) interacted with the additional element (a flow cytometry apparatus) in a manner that provided an improvement in the additional element (a flow cytometry apparatus). Thus, the fact pattern in XY, LLC v. Trans Ova Genetics is distinct from the instant case because the instant case does not provide an interaction between the judicial exceptions and the additional element (the computer) in a manner that provides an improvement in the additional element (the functioning of a computer).
Applicant argues claim 1 provides a technique for custom-parameterized dimensionality reduction which provide increased efficiency and accuracy of various predictive data analysis systems being utilized in complex prediction domains. Applicant further argues claim 1 recites a combination of additional elements that improve a technical field such that the claim as a whole integrates the abstract idea into a practical application (Reply p. 18-21)
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.05(a)(II) that “an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”. Applicant has not provided how the judicial exception provides an improvement that is realized in the additional element of the claim (a computer). Applicant provides that the claim improves dimensionality reduction techniques through increased efficiency and accuracy. Though the claimed invention may be an improvement over previous dimensionality reduction techniques, the steps fall under abstract ideas of mental processes and mathematical concepts. Thus, the claims provide an improvement to an abstract idea which does not constitute as an improvement in a technology.
Conclusion
No claims are allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN EDWARD HAYES whose telephone number is (571)272-6165. The examiner can normally be reached M-F 9am-5pm.
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/J.E.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685