DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant's amendments and remarks, filed, 05/05/2026, are acknowledged. Rejections and/or objections not reiterated from previous office actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1, 3-5, 7-19, 33, 34, 35, 36 are under examination.
Claims 2, 6, and 20-32 are cancelled. Claim 36 is newly added.
Priority
This application is a U.S. National Stage Application filed under 35 U.S.C. §371 of International Application No. PCT/US2019/062561, filed November 21, 2019 (Published as WO 2020/112478), which claims the benefit of and priority to U.S. Provisional Patent Application No. 62/773,028 filed November 29, 2018 and U.S. Provisional Patent Application No. 62/783,733 filed December 21, 2018.
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, 3-5, 7-19, 33, 34, 35, 36 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.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and ‘as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355).
Guidance: Step 1.
Under the broadest reasonable interpretation, the claimed invention (claims 1 and 34) being representative) is directed to a method and system for performing a process. Accordingly, the invention falls within one of the four statutory categories.
A. Guidance Step 2A, Prong 1
The Revised Guidance instructs us first to determine whether any judicial exception to patent eligibility is recited in the claim. The Revised Guidance identifies three judicially-excepted groupings identified by the courts as abstract ideas: (1) mathematical concepts, (2) certain methods of organizing human behavior such as fundamental economic practices, and (3) mental processes. In this case, the claimed steps that are part of the abstract idea are as follows:
b) downsampling the class-imbalanced data set to generate a downsampled data set, wherein the downsampling results in the majority data class having an equivalent or substantially equivalent number of observations as the minority data class; and
c) outputting a survival model trained with the downsampled data set by performing cross-validation on the downsampled data set with a survival analysis;
d) applying an elastic net penalty to the survival model;
e) determining an AUC, sensitivity, specificity, and/or C-index of the survival model having the penalty; wherein the observation comprises an event or no event at a specific time value…;
Mental Process
With regards to downsampling and applying, these steps are recited at a high level of generality (without any technological details directed to how they are performed). In addition, scientists are capable of downsampling data (i.e. reducing the number of data points) or applying a model using their brains or a pencil and paper. As such, this step encompasses a mental process of observing data and/or manipulating data. MPEP 2106.04(a)(2), section III.
With regards to outputting (a trained survival model), this step is recited at a high level of generality (without any technological details directed to the structure of the model or how the training was previously performed). In addition, scientists are capable of outputting and/or training model using their brains or a pencil and paper (since training under the BRI is merely learning or adjusting model parameters). As such, this step encompasses a mental process of observing data and/or manipulating data. MPEP 2106.04(a)(2), section III.
With regards to determining, this step is recited at a high level of generality (without any technological details directed to how it is performed). In addition, scientists are capable of determining statistical parameters (e.g. AUC, sensitivity, etc) using their brains or a pencil and paper. As such, this step encompasses a mental process of evaluating data. MPEP 2106.04(a)(2), section III.
It is important to note that “claims that recite performing information analysis as well as the collection and manipulation of information related to such analysis, have been determined by our reviewing court to be an abstract concept that is not patent eligible”. See SAP, 898 F.3d, 1165, 1167, 1168 (Claims reciting "[a] method for providing statistical analysis" (id. at 1165) were determined to be "directed to an abstract idea" (id. at 1168)); see also Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat'l Ass 'n, 776 F.3d 1343, 1345, 1347 (Fed. Cir. 2014). [Step 2A, Prong 1: YES].
Mathematical Concept
With regards to outputting a survival model, this step is recited at a high level of generality (without any technological details directed to the structure of the model or how the training was previously performed). Moreover, to the extent that applicant intends for this step to encompass “training” a model, the artisan would recognize that such training is an explicit mathematic process of assigning and adjusting weights of parameters that define the structure of the model. This position is supported by Goodfellow et al. ("Explaining and harnessing adversarial examples”, Published as a conference paper at ICLR 2015, pp.1-11), which explicitly teaches that training a model explicitly requires mathematically relating data [Sections 3 and 4]. As such, this step recites mathematical correlations and/or calculations. MPEP 2106.04(a)(2) Section I.
With regards to applying (an elastic net penalty), the artisan would recognize that an elastic net penalty is a mathematical function and thus applying this a trained model results in a mathematical calculation (to fit model parameters). As such, this step recites mathematical correlations and/or calculations. MPEP 2106.04(a)(2) Section I. [Step 2A, Prong 1: YES]. Similarly, with regards to said determining, this step requires calculating one or more statistical metrics (e.g. AUC, sensitivity, specificity) and a review of the specification [0008, 0011] also teaches various types of cross-validation including k-fold, Generalized Monte Carlo, leave-p-out cross-validation, or bootstrapping methods associated with numerical calculations. Moreover, Kamarudin et al. (cited below in the rejection under 35 USC 103) teaches various methods for modeling disease risk using survival functions [see entire], wherein the model is well-defined in terms of mathematical parameters. LeDell et al. (Electron J Stat. 2015 ; 9(1): 1583–1607) also teaches that cross-validation and determining metrics such as AUC and sensitivity must be mathematically calculated (e.g. Sensitivity = TP / (TP + FN)) [Section 2]. As such, these steps recite mathematical correlations and/or calculations. MPEP 2106.04(a)(2) Section I. [Step 2A, Prong 1: YES].
Similar to the ineligible claims at issue for In re: Board of Trustees of the Leland Stanford Junior University, 991 F.3d 1245 (Fed. Cir. 2021), the instant claims are written effectively as a method for mathematically manipulating or relating data to ascertain additional data. While no specific equation is being claimed, Applicant is reminded that there is no particular word or set of words that indicates a claim recites a mathematical calculation. See MPEP 2106.04(a)(2). Therefore, when read in light of applicant’s own specification, the claims are directed to mathematical concepts. See MPEP 2106.04 and 2106.05(II). [Step 2A, Prong 1: YES].
B. Guidance Step 2A, Prong 2
This part of the eligibility analysis evaluates whether the claim includes any additional steps/elements that integrate the recited judicial exception into a practical application of the exception. In this case, the additional steps/elements that are not part of the abstract idea are as follows:
a) acquiring a class-imbalanced data set, wherein the class-imbalanced data set comprises biological data from a plurality of subjects, wherein the biological data of each subject includes an observation, a time value, and a plurality of clinical measurements…;
With regards to said acquiring, this step is recited at a high level of generality (without any technological details directed to how it is performed) and results in collecting a particular type of data for use by the abstract idea. Accordingly, this step amounts to extra-solution activity and is not indicative of an integration into a practical application. See MPEP 2106.05(g).
With regard to the claimed processor and memory devices (claim 34), these features are generically recited and merely used as tools to obtain information and perform the abstract idea. Moreover, applicant is reminded that “generic computer components such as a computer and database do not satisfy the inventive concept requirement.” See MPEP 2106.05(f) and 2106.05(h). In addition, the courts have explained that the use of generic computer elements do not alone transform an otherwise abstract idea into patent-eligible subject matter. See DDR Holdings (Fed. Cir. 2014). Therefore, the additionally recited steps/elements amount to insignificant extra-solution activity that does not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Even when viewed in combination, these additional steps/elements do not integrate the recited judicial exception into a practical application. See MPEP 2106.04(d)(1) for a list of considerations when evaluating whether additional elements integrate a judicial exception into a practical application. [Step 2A, Prong 2: NO].
C. Guidance Step 2B:
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. In this case, the claims do not include additional steps and/or elements appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception(s) for the following reasons:
As discussed above, the claim does not recite any additional steps/elements that amount to significantly more than the judicial exception. In addition, a review of the specification teaches routine and conventional computer systems and architecture (including a data acquisition module) [0058-0060, Figure 2] for acquiring and processing the claimed data as discussed above. Therefore, even upon reconsideration, there is nothing unconventional with regards to the above non-abstract steps/elements being claimed. See MPEP 2106.05(d)(Part II). Thus, the independent claim(s) as a whole do not amount to significantly more than the exception itself. Therefore, the claim(s) is/are not patent eligible. [Step 2B: NO].
Dependent Claims
Dependent claims 3-5, 7-19, 33, 35, 36 have also been considered under the two-part analysis but do not include additional steps/elements appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception(s) for the following reasons. Regarding claim(s) 3-5, 7-19, 33, 35, 36, these are all directed to limitations that further limit the specificity of the abstract idea set forth above or the data being used by the abstract idea. Therefore, these claims also encompass a mental process and/or mathematical concepts for reasons discussed above in the Step 2A (prong 1) analysis. Therefore, the claims as a whole are not patent eligible.
Response to Arguments
Applicant’s arguments, filed 05/05/2026, have been fully considered but are not persuasive for the following reasons.
Applicant again argues that the claimed downsampling and outputting steps do not recite a mental process. In response, with regards to downsampling, this step is recited at a high level of generality without any technological details directed to how it is performed. In addition, scientists are capable of downsampling data using their brains or a pencil and paper (since downsampling is merely reducing the number of data points under the BRI) and applicant has not presented any evidence to the contrary. As such, the examiner maintains that this step encompasses a mental process of observing data and/or manipulating data. MPEP 2106.04(a)(2), section III. With regards to outputting (a trained survival model), this step is recited at a high level of generality (without any technological details directed to the structure of the model or how the training was previously performed). In addition, scientists are capable of outputting and/or training a model using their brains or a pencil and paper (since training under the BRI is merely learning or adjusting model parameters) and applicant has not presented any evidence to the contrary. As such, the examiner maintains that this step encompasses a mental process of observing data and/or manipulating data. MPEP 2106.04(a)(2), section III. In addition, the Office's eligibility guidance does not set limit on the size of the data or number of calculations that can or cannot be performed mentally. MPEP § 2106.04(a)(2)III. The specification also provides sufficient evidence that the claims are directed to an abstract idea since the specific descriptions provided for accomplishing these tasks only involve data reception and analysis [0010, 0011, 0039]. For at least these reasons, the examiner maintains that the claims indeed recite a mental process.
Applicant additionally argues that the claims have not been properly evaluated within the two-step Alice framework. In response, the examiner has clearly identified the claim steps that recite abstract ideas and providing sufficient reasoning as to why the claims steps encompass concepts including observation, evaluation, judgment, and opinion, as well a mathematical concept (Step 2A, prong 1 analysis). Therefore, the examiner maintains that he has properly applied the two-step analysis as explained in MPEP 2106.04(a)(2), subsection III.
Applicant additionally argues that the claims are patent eligible in view of the August 2025 Memorandum, asserting that the amended “outputting” step amounts to “A.I. innovation”. In response, applicant is reminded that the August 2025 Memo explicitly states that “This memorandum is not intended to announce any new USPTO practice or procedure and is meant to be consistent with existing USPTO guidance.” That being said, the claimed “outputting” step is recited at a high level of generality without any technological details directed to the structure of the model or how the training was previously performed and broadly result in outputting a (trained) model by performing cross-validation. Contrary to applicant’s assertion, the amended claim also does not recite any positive process limitation directed to “training” a model (i.e. a machine learning step) but merely suggest this feature (since the model has been “trained” at some previous point in time). However, for reasons discussed below, claims that merely suggest a limitation without positively reciting a limitation are not given patentable weight. See MPEP 2111.04. To further support this distinction, the claims in Ex parte Desjardins were drawn to a method of training a machine learning model trained on a first task, comprising determining and assigning parameter weights, training the model again using different data on a different task, and adjusting parameters and weights, while protecting performance of the first task. Therefore, it is the examiner’s position that the instant claims are not commensurate in scope with those of Ex parte Desjardins in terms of the structure of the models used or the steps required. Accordingly, the examiner maintains that the claims are not patent eligible in view of the August 2025 Memo. See also MPEP 2106.04(d)(1) and MPEP 2106.05(a).
Applicant additionally argues that the claims have not been considered as a whole and the claimed invention integrates the abstract into a practical application by improving the functioning of computer technology and inventing operations that improve survival models and their predictions (citing the specification para. 0004, 0047, 0053, 0092-98, and Tables 2 and 3). In response, the MPEP is clear that the word "improvements" in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B. MPEP 2106.04(d)(1). In this case, a review of the specification does not provide any objective evidence to support the case that the claims result in an improvement to the functioning of a computer. At best, the cited sections of the specification amount to nothing more than a bare assertion of an improvement (without the detail necessary to be apparent to a person of ordinary skill in the art). In addition, Applicant has failed to identify any steps/elements appended to the abstract idea that provide for a new machine learning technology (Step 2A, prong 2 or Step 2B) or provided any evidence of an unconventional combination of steps. The abstract idea is also implemented on generic components that are recited at a high level of generality and are well-understood, routine, and conventional. As such, there is nothing in the specification to support applicant’s position that the claimed invention is directed to an improvement in a particular machine, computer, or computer functionality (as in Enfish). Therefore, applicant is essentially arguing that the improvement (namely the ‘survival model’) is entirely in the realm of abstract ideas and that abstract idea is providing the improvement (by providing “better data”). However, Applicant is reminded that the claimed invention’s use of the ineligible concept to which it is directed (i.e. the abstract idea) cannot supply the inventive concept that renders the invention ‘significantly more’ than that ineligible concept.” BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290 (Fed. Cir. 2018). It is also well settled that mere computer-based efficiency does not save an otherwise abstract method. Bancorp Servs. L.L.C. v. Sun Life Assur. Co. of Canada (US.), 687 F.3d 1266, 1277-78 (Fed. Cir. 2012) (explaining that performance by computer of operations that previously were performed manually or mentally, albeit less efficiently, does not convert a known abstract idea into eligible subject matter). Similarly, the courts have also instructed that “[t]he different use of a mathematical calculation, even one that yields different or better results, does not render patent eligible subject matter.” Board Of Trustees Of Leland Stanford Junior University, 991 F.3d 1245, 1251 (Fed. Cir. 2021). For these reasons, the examiner maintains that the claims do not provide an improvement to the technology (under Step 2A, prong 2, or Step 2B). See MPEP 2106.04(d)(1).
Applicant additionally argues that the claims are patent eligible in view of Ex parte Desjardins. In response, unlike the claims in Ex parte Desjardins, the instant claims do not recite an improvement to computer functionality and do not delineate steps through which machine learning technology achieves an improvement. To further illustrate this point, as discussed above, Ex parte Desjardins was drawn to a method of training a machine learning model trained on a first task, comprising determining and assigning parameter weights, training the model again using different data on a different task, and adjusting parameters and weights, while protecting performance of the first task. Further, in Desjardins, the retraining of the particular model changed the structure of that model in a way that provided "'[a]n improvement in the functioning of a computer, or an improvement to other technology or technical field,' as discussed in MPEP §§ 2106.04(d)(l) and 2106.05(a). Moreover, the independent claim in Ex parte Desjardins contained specific limitations as to how at least some aspects of the asserted improvements are achieved: "When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation." See Ex parte Desjardins, page 9. In contrast, the instant claims do not clearly set forth the link between the data gathered, the initial training of the model, the structure of the model, and how subsequent training affects the model structure to obtain the desired results or asserted improvement. For at least these reasons, applicant’s arguments are not persuasive. As such, the examiner maintains that the claims do not integrate the recited judicial exception into a practical application. Similar to Recentive v. Fox, applicant is reminded that AI and machine learning claims need to demonstrate a genuine technological advancement beyond merely applying generic ML to a new use case or achieving increased speed and efficiency to be considered patent-eligible under Section 101. See also MPEP 2106.04(d)(1) for a list of considerations for evaluating whether additional elements integrate a judicial exception into a practical application.
Applicant additionally argues that the ‘applying’ step further amounts to an improvement to the model. In response, the ‘applying’ step has been interpreted as part of the abstract idea (Step 2A, prong 1 analysis). Accordingly, this argument is not persuasive for the reasons set forth above. Additionally, applicant has not provided any evidence that there is anything unconventional with regards to the additional steps/elements appended to the abstract idea. Indeed, the claim relies upon routine and conventional data collection steps in combination with the abstract idea steps. That is not the kind of “technological” improvement that suffices for patent eligibility. See also Amdocs Ltd. v. Openet Telecom, Inc., 841 F.3d 1288, 1300, 1302 (Fed. Cir. 2016). For these reasons, the examiner maintains that the claims do not provide an improvement to the technology (under Step 2A, prong 2, or Step 2B). See MPEP 2106.04(d)(1). For at least these reasons, the rejection is maintained.
Claim rejections - 35 USC § 112, 2nd Paragraph
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 1, 3-5, 7-19, 33, 34, 35 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 pre-AIA the applicant regards as the invention. Claims that depend directly or indirectly from claim(s) 1 and 34 is/are also rejected due to said dependency.
Claims 1 and 34 recite “outputting a survival model trained with the downsampled data set by performing cross-validation on the downsampled data set with a survival analysis.” Firstly, the use of “past-tense” language directed to how the model was obtained at some previous time point renders the claim indefinite because it suggests active steps for “training” a model without explicitly requiring any such limitations. Applicant is reminded that claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed. MPEP 2111.04. Moreover, the artisan would recognize that performing cross-validation is a statistical technique used to evaluate a model’s performance, and is thus distinct from training a model. As such, it is unclear in what way said model was “trained…by performing cross-validation”. Accordingly, the claim is also indefinite as it is unclear what computational operations are encompassed by the claimed “outputting”. Clarification is requested via amendment.
Claims 1 and 34 recite “applying an elastic net penalty to the generated survival model”. While the artisan would generally understand that an “elastic net penalty” generally relates to a term added to a survival model's loss function (e.g. to optimize coefficients), a review of the specification does not provide any limiting definition for the claimed “survival model” such that it is associated with specific equations, functions, or parameters. As such, it remains unclear in what way the “elastic net penalty” is applied to the generically recited survival model. The specification discloses a Cox model [0080] and that [0082] “survival analysis was combined with the elastic net penalty by using the Cox elastic net model…The Cox elastic net model merges the standard Cox proportional hazards model with elastic net penalization, allowing use of survival techniques to develop a classifier, plus the benefits of penalized regression. However, this is not commensurate in scope with what is claimed and Applicant is reminded that it is improper to import narrowing limitations found in the specification into the claims. See MPEP 2111.01. Accordingly, it remains unclear what computational operations are encompassed by the claimed “applying” step. Clarification is again requested via amendment as applicant has not provided any illuminating arguments or clarifying amendments.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
The following rejection is modified in view of applicant’s amendments.
Claims 1, 3-5, 7-19, 33, 34, 35, 36 are rejected under 35 U.S.C. 103(a) as being unpatentable over Rubenstein et al. (US2018/0226153A1; Pub. Date: 08/09/2018) in view of Zou et al. (J. R. Statist. Soc. B, 2005, 67, Part 2, pp.301-320), Kamarudin et al. (BMC Medical Research Methodology, 2017,17:53, pp.1-19), and He et al. (IEEE Transactions On Knowledge And Data Engineering, 2009, Vol. 21, No. 9, pp. 1263-84).
Regarding claim(s) 1 and 34, Rubenstein teaches systems and methods for predicting treatment-regimen-related outcomes (e.g., risks of regimen-related toxicities). In particular, Rubenstein teaches obtaining one or more training datasets using a plurality of different types of biological data including non-genetic data (e.g., the clinical data), genetic data (e.g., the selected SNPs and the filtered SNPs), observations, and time [0044, 0063, 0064, Figure 2, Tables 6-18], which are construed as class-imbalanced data sets given the breadth of the claims. Rubenstein teaches categorizing data into minority and majority classes [0078, 0044, 0084]. Rubenstein teaches both down-sampling data (e.g., sampling the majority class to create balance with the minority class) and up-sampling data (e.g., selecting additional minority class subjects with replacement to increase the minority class size) during the process of building a model to adjust for model imbalances [0044, 0084], which reads on downsampling, as claimed. Rubenstein teaches using down-sampling data as training data for training the predictive model [0044], performing cross-validation on the training data sets to determine the optimal tuning parameters for the predictive model [0045, 0085], and training the predictive model [0007-0010]. Rubenstein teaches a model building process that includes selecting (i.e. generating) a survival model that predicts regimen-related outcomes as a function of health and survival time based on penalized logistic regression, random forests (RF), and C5.0 [0064, 0088, Figure 3], which reasonably suggests outputting a survival model based on cross-validation.
Rubenstein does not specifically teach applying an elastic net penalty to the survival model, as claimed. However, Rubenstein suggests this feature by using penalized logistic regression for model parameter selection, as set forth above, wherein the penalized regression model is broadly interpreted as a net penalty model absent any limiting definition or specific equations to the contrary. Moreover, Zou explicitly teaches methods for optimizing model parameters using the elastic net penalty [See entire]. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n) [Summary]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rubenstein by applying an elastic net penalty, as claimed, since such techniques are routine and conventional in the art of model selection, as taught by Zou. The motivation would have been model optimization when the number of predictors is greater than the number of observations.
Rubenstein does not specifically teach determining an AUC, sensitivity, specificity, and/or C-index of the survival model, wherein AUC, sensitivity, specificity, and/or C-index of the survival model is closer to 1 than a AUC, sensitivity, specificity, and/or C-index of a survival model where the class-imbalanced data set was not downsampled prior to the survival analysis”. However, Rubenstein at a minimum suggests this limitation by selecting final predictive models based on sensitivity and specificity calculations [0052 and Figure 3]. It is additionally noted that limitations directed to intended use or suggestion of steps are not given patentable weight.
Moreover, Kamarudin teaches a plurality of different survival functions and methods for calculating a plurality of different model parameters associated with survival including ROC values, sensitivity, and specificity [Table 1]. Alternatively, He et al. (IEEE Transactions On Knowledge And Data Engineering, 2009, Vol. 21, No. 9, pp. 1263-84) teaches methods of analyzing imbalanced data sets that include sampling techniques (up/over and down/under sampling) and ROC calculations, wherein downsampling is associated with specific learning algorithms that attempt to rebalance the data [Section 3.1.2]. He also teaches a kernel-based algorithm that integrates the concepts of cross validation, and area under curve (AUC) and sensitivity evaluation metrics to develop an objective function as a selection mechanism of the most optimal kernel model [Section 3.3.3 and Section 4.2].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rubenstein by determining an AUC, sensitivity, specificity, and/or C-index of a survival model, wherein AUC, sensitivity, specificity, and/or C-index of the survival model is closer to 1 than a AUC, sensitivity, specificity, and/or C-index of a survival model where the class-imbalanced data set was not downsampled prior to the survival analysis, since Rubenstein suggests generating a survival model using cross-validation techniques, as set forth above, and since AUC, sensitivity, and specificity are routine and conventional performance metrics for evaluating models (using imbalanced data), as taught by Kamarudin and He. In addition, one of ordinary skill in the art would recognize that these performance metrics are variable and thus are easily optimized for model selection. The rationale would have been the predictable use of prior art elements according to their established functions. KSR, 550, U.S. at 417. Additional motivation would have been to perform routine optimization to identify the most accurate model.
Regarding claim(s) 3, 4, 7-19, 33, 35, 36, the combination of Rubenstein, Zou, Kamarudin, and He teaches or suggests all aspects of these claims for the following reasons. Regarding claim(s) 3, 4, Rubenstein teaches class-imbalanced survival data associated with disease [0038, 0044, 0063, 0064, Figure 2, Tables 6-18]. Regarding claim(s) 7, 11, Rubenstein teaches 10-fold cross validation procedures, as set forth above. Regarding claim(s) 8, 9, 10, 33, Rubenstein teaches model features that include clinical factors including medical history, gender, age, ethnicity, and demographic information [0038] as well as proteomic information, transcriptomic information, and metabolomic information [0034]. Kamarudin additionally teaches Cox models throughout [page 9]. Regarding claim(s) 35, Rubenstein teaches calculating risk for patients with cardiac dysfunction [0056], which at a minimum suggestes myocardial infarction. Regarding claim(s) 36, Rubenstein and Zou make obvious applying an elastic penalty to a predictive model, as set forth above, and Rubenstein additionally teaches using their predictive model for predicting regimen-related outcomes [ref. claim 1]. Regarding claim(s) 12-19, Rubenstein, Zou, He, and Kamarudin do not specifically teach the various claimed percentages of majority class data and minority class data. However, classification percentages are considered results-effective variables that are routinely optimizable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the teachings of Rubenstein and Kamarudin by using the various claimed percentages of majority and minority groups with a reasonable expectation of success, since Rubenstein already teaches that changes in the amount of majority and minority groupings directly affects model performance [0044], and since one of skill in the art would recognize that such percentages are variable and could easily be optimized based on the disease under investigation. Additional motivation would have been to perform routine optimization to yield better results, as taught by Rubenstein [0044].
Response to Arguments
Applicant’s arguments, filed 05/05/2026, have been fully considered but are not persuasive for the following reasons. The above rejection has been modified in view of applicant’s amendments.
Applicant argues that Rubenstein does not teach or suggest a “survival model”. In response, it is noted with particularity that the claimed “survival model” is generically recited and is not limited to any particular mathematical equation or parameters. In other words, any model that is trained using downsampled data and capable of predicting events reads on this model under the BRI. That being said, Rubenstein teaches a model that predicts regimen-related outcomes as a function of health and survival time based on penalized logistic regression, random forests (RF), and C5.0 [0064, 0088, Figure 3]; using down-sampling data as training data for training the predictive model [0044; 0007-0010]; performing cross-validation on the training data sets to determine the optimal tuning parameters for the predictive model [0045, 0085]. As such, Rubenstein’s teaching of a predictive model at a minimum suggests the claimed survival model absent any limiting definition to the contrary.
Applicant’s argument that the specification provides evidence against this interpretation is not persuasive because [0050] does not provide any limiting definition to the contrary. Moreover, applicant is reminded that is improper to import narrowing limitations into the claims. MPEP 2111.01. For at least these reasons, the rejection is maintained.
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 extension fee 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 date of this final action.
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/PABLO S WHALEY/Primary Examiner, Art Unit 3619
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