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 .
Specification
The lengthy specification has not been checked to the extent necessary to
determine the presence of all possible minor errors. Applicant's cooperation is
requested in correcting any errors of which applicant may become aware in the
specification.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
As to claim 1,
Step 1: Claim 1 is directed to a method. Therefore, the claim is eligible under Step 1 for being directed to processes.
Step 2A Prong One
Claim 1 recites
defining a skedastic function model, wherein defining the skedastic function model is performed independently of data that will be applied to a target trial; (mental process)
designing trial parameters for the target trial based in part on the skedastic function model; (mental process)
applying the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters; (mental process)
computing standard errors for the at least one minimizing coefficient; (mental process)
quantifying, using the standard errors, values for uncertainty associated with the target trial; (mental process) and
updating the trial parameters according to the uncertainty. (mental process)
The claimed concept is a method of trial parameters based on mathematic relationship directed to “Mental Process” and/or “Mathematical Concepts” grouping. These limitations can be performed in a human mind or using pen and paper.
Therefore, claim 1 is an abstract idea.
Step 2A Prong Two
The claim recites additional elements such as “model”. Simply implementing the abstract idea on a generic computer (e.g. executing as a model or simulation) is not a practical application of the abstract idea. Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See applicant’s specification Fig. 8-9 [0148] for generic computer description.
The judicial exception is not integrated into a practical application.
Step 2B:
The same analysis of Step 2A Prong Two applies here in 2B. The present claim does not recite any limitation that would integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(d).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the claim limitations do not recite a combination of additional elements that tie or “integrate the invention into a practical application”.
Thus, claim 1 is not patent eligible. Same conclusion for dependent claims of claim 1. See below.
2. The method of claim 1, wherein the standard errors are heteroskedasticity-consistent standard errors. (data description)
3. The method of claim 1, wherein the skedastic function model is defined based on historical data. (mental process)
4. The method of claim 3, wherein defining the skedastic function model comprises:
applying parameters of the skedastic function model to a loss function for data from the target trial, to derive at least one minimizing coefficient, wherein the at least one minimizing coefficient includes a treatment effect coefficient; (mental process)
computing standard errors for the at least one minimizing coefficient; (mental process)
calculating: one or more predicted outcomes for the target trial; and one or more predicted outcomes for the historical data; and defining the skedastic function model based on:
residuals corresponding to the one or more predicted outcomes for the historical data;
and variances corresponding to the one or more predicted outcomes for the target trial. (mental process)
5. The method of claim 4, wherein the target trial is a randomized controlled trial; wherein predicted outcomes for the historical data are digital twin outputs; wherein predicted outcomes for the target trial are digital twin outputs; and wherein minimizing coefficients are treatment effect coefficients. (mental process)
6. The method of claim 4, wherein the historical data comprises at least one selected from the group consisting of control arm data from historical control arms, patient registries, electronic health records, and real world data. (mental process)
7. The method of claim 1, wherein the loss function is a weighted least squares loss function. (mental process)
8. The method of claim 7, wherein at least one weight quantity of the weighted least squares loss function is inversely proportional to a predicted variance of outcomes of a participant in the target trial. (mental process)
9. The method of claim 1, wherein data applied to target trials comprises panel data collected from participants to a previous trial based on individual characteristics of the participants. (mental process)
10. The method of claim 1, wherein the expected outcome is obtained through at least one of the group consisting of a digital twin and a prognostic model. (mental process)
Same conclusion for independent claims 11 and dependent claims. Thus, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 1-3, 7, 9-13, 17, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fisher et al (US 2021/0057108 A1), hereinafter Fisher, in view of Bianchi et al (NPL: Asymptotic Properties and Variance Estimators of the M-quantile Regression Coefficients Estimators, 2015), hereinafter Bianchi.
Claim 1. A method for estimating treatment effects for a target trial, the method comprising:
designing trial parameters for the target trial based in part on the skedastic function model;
Fisher: [0041] “Historical borrowing refers to incorporating data from the control arms of previously completed trials into the analysis of a new trial. …”
Fisher: [0043] “In several embodiments, simulated subject records can be sampled from probabilistic generative models that can be trained on various data, such as (but not limited to) one or more of historical, registry, and/or real world data. Such models can allow one to extrapolate to new patient populations and study designs.”
applying the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters;
Fisher: [0073-0077] “Some methods estimate treatment effects using GLMs while adjusting for covariates. For example, one may perform a regression of the final outcome in the trial against the treatment indicator and a measure of disease severity at the start of the trial. As long as the covariate was measured before the treatment was assigned in a randomized controlled trial, then adjusting for the covariate will not bias the estimate for the treatment effect in a frequentist analysis. When using covariate adjustment, the statistical power is a function of the correlation between the outcome and the covariate being adjusted for; the larger the correlation, the higher the power. In theory, the covariate that is most correlated with the outcome that one could obtain is an accurate prediction of the outcome. Therefore, another method to incorporate generative models into RCTs in accordance with a variety of embodiments of the invention is to use generative models to predict outcomes and to adjust for the predicted outcomes in a GLM for estimating the treatment effect. …. As above, processes in accordance with several embodiments of the invention can use generative models that generate panel data so that a single generative model may be used for analyses of many outcomes in a given trial (e.g., primary, secondary, and exploratory endpoints as well as safety information). In a number of embodiments, rather than predictions for a given outcome, predictions of multiple outcomes derived from a generative model may all be included in a GLM for a particular outcome. Samples drawn from the generative models in accordance with several embodiments of the invention can be conditioned on the characteristics of a subject at the start of the trial, also referred to as digital twins of that subject. …”
Examiner considers the correlation function is the opposite of the loss functions.
Examiner considers “the larger the correlation” correspond to “a loss function to derive at least one minimizing outcome coefficient”.
computing standard errors for the at least one minimizing coefficient;
Fisher: [0073-0074] “Some methods estimate treatment effects using GLMs while adjusting for covariates. For example, one may perform a regression of the final outcome in the trial against the treatment indicator and a measure of disease severity at the start of the trial. As long as the covariate was measured before the treatment was assigned in a randomized controlled trial, then adjusting for the covariate will not bias the estimate for the treatment effect in a frequentist analysis. When using covariate adjustment, the statistical power is a function of the correlation between the outcome and the covariate being adjusted for; the larger the correlation, the higher the power. In theory, the covariate that is most correlated with the outcome that one could obtain is an accurate prediction of the outcome. …”
quantifying, using the standard errors, values for uncertainty associated with the target trial;
Fisher: [0076] “The above equation can be generalized to various applications and implementations. The terms involving the b coefficients represent the treatment effect, which may depend on the baseline covariates x.sub.0. The terms involving the c coefficients represent potential bias in the generative model, which may depend on the baseline covariates x.sub.0. The terms involving the d coefficients represent potential baseline differences between the treatment and control groups in the trial. The terms involving the z coefficients reflect that the relationship between the predicted and observed outcomes may be affected by the treatment. The model can be fit using any of a variety of methods for fitting GLMs. In a number of embodiments, uncertainties in the coefficients can be estimated analytically. Alternatively, or conjunctively, processes in accordance with many embodiments of the invention can estimate uncertainties using a bootstrap by repeatedly resampling the data (with replacement) and re-fitting the model; the uncertainties can be the standard deviations of the coefficients computed by this resampling procedure. In some embodiments, point estimates for the treatment effect and estimates for their uncertainty can be used to perform a hypothesis test in order to create a decision rule.”
updating the trial parameters according to the uncertainty.
Fisher: [0077] “In some embodiments, variances of the outcomes can be modeled through another GLM that adjusts for the variance of the outcome that is predicted by the generative model.”
Examiner considers “adjusting” correspond to “updating”.
Fisher does not appear to explicitly disclose defining a skedastic function model, wherein defining the skedastic function model is performed independently of data that will be applied to a target trial;
However, Bianchi (page 2417) defining a skedastic function model, wherein defining the skedastic function model is performed independently of data that will be applied to a target trial;
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Examiner considers “skedastic function model” as “a statistical model comprises conditional variance” (i.e. skedastic function) and “trial data” as “sample data”.
Fisher and Bianchi are analogous art because they are from the “same field of endeavor” statistical analysis.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fisher and Bianchi before him or her, to modify the generative model of Fisher to include the regression coefficients estimator of Bianchi because this combination improves the accuracy of the estimation.
The suggestion/motivation for doing so would have been Bianchi (page 2417)
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Therefore, it would have been obvious to combine Fisher and Bianchi to obtain the invention as specified in the instant claim(s).
Claim 2 and 12
wherein the standard errors are heteroskedasticity-consistent standard errors.
Bianchi (page 2422)
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Regarding Claim 11, the same ground of rejection is made as discussed above for substantially similar rationale.
In addition, Claim 11 recites “non-transitory computer-readable medium for estimating treatment effects for a target trial, wherein the program instructions are executable by one or more processors to perform a process that comprises:”.
Fisher discloses non-transitory computer-readable medium for estimating treatment effects for a target trial, wherein the program instructions are executable by one or more processors to perform a process that comprises: on [0100] Fig. 11.
Claim 3 and 13
wherein the skedastic function model is defined based on historical data.
Bianchi (page 2417)
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Examiner considers “observable data” as “historical data”
Claim 7 and 17
wherein the loss function is a weighted least squares loss function.
Bianchi: (page 2418)
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Claim 9 and 19
wherein data applied to target trials comprises panel data collected from participants to a previous trial based on individual characteristics of the participants.
Fisher: [0049-0050] “Process 200 generates (210) digital subject data using generative models. Generative models in accordance with certain embodiments of the invention can be trained to generate potential outcome data based on characteristics of an individual and/or a population. Digital subject data in accordance with several embodiments of the invention can include (but is not limited to) panel data, outcome data, etc. In numerous embodiments, generative models can be trained directly on a specific outcome p(y|x.sub.0). For example, if a goal of using the generative model is to increase the statistical power for the primary analysis of a randomized controlled trial then it may be sufficient (but not necessary) to only use a model of p(y|x.sub.0). Alternatively, or conjunctively, generative models trained to generate panel data that can be used in the analysis of a clinical trial. Data for a subject in a clinical trial is typically a panel; that is, it describes the observed values of multiple characteristics at multiple discrete timepoints (e.g. visits to the clinical trial site). For example, if a goal of using the generative model is to reduce the number of subjects in the control group of the trial, or as an external comparator for a single arm trial, then generated panel data in accordance with many embodiments of the invention can be used to perform many or all of the analyses of the trial.”
Claim 10 and 20
wherein the expected outcome is obtained through at least one of the group consisting of a digital twin and a prognostic model.
Fisher: [0036] “Examples of uses for generative models in the analysis of clinical trials in accordance with various embodiments of the invention are illustrated in FIG. 1. The first example 105 illustrates that generative models, digital subjects, or digital twins can be used to increase the statistical power of traditional randomized controlled trials.”
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fisher et al (US 2021/0057108 A1), hereinafter Fisher, in view of Bianchi et al (NPL: Asymptotic Properties and Variance Estimators of the M-quantile Regression Coefficients Estimators, 2015), hereinafter Bianchi, further in view of Romano et al (NPL: Resurrecting weighted least squares, 2017), hereinafter Romano.
Claim 8 and 18
Fisher and Bianchi do not appear to explicitly disclose wherein at least one weight quantity of the weighted least squares loss function is inversely proportional to a predicted variance of outcomes of a participant in the target trial.
However, Romano disclose wherein at least one weight quantity of the weighted least squares loss function is inversely proportional to a predicted variance of outcomes of a participant in the target trial (page 12)
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Fisher, Bianchi and Romano are analogous art because they are from the “same field of endeavor” statistical analysis.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fisher, Bianchi and Romano before him or her, to modify the generative model of Fisher to include the regression coefficients estimator of Bianchi and weighted least square estimator of Romano because this combination improves the accuracy of the estimation.
The suggestion/motivation for doing so would have been Romano (page 2)
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Therefore, it would have been obvious to combine Fisher, Bianchi and Romano to obtain the invention as specified in the instant claim(s).
Allowable Subject Matter
Claims 4-6 and 14-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and to overcome rejections under 35 U.S.C. 101.
The following is a statement of reasons for the indication of allowable subject matter:
Fisher et al (US 20210057108 A1) teaches a method for determine treatment effects for a randomized control trial (RCT) using data sampled from a generative model, design RCTs, and/or determine decision rules for treatments.
Bianchi et al (NPL: Asymptotic Properties and Variance Estimators of the M-quantile Regression Coefficients Estimators, 2015) teaches asymptotic properties for the M-quantile regression coefficients estimators in the case of i.i.d. data with stochastic regressors, paying attention to adjustments due to the first-step scale estimation.
Romano et al (NPL: Resurrecting weighted least squares, 2017) teaches asymptotically valid inference in regression models based on the weighted least squares (WLS) estimator can be obtained even when the model for reweighting the data is misspecified. Like the ordinary least squares estimator, the WLS estimator can be accompanied by heteroskedasticity-consistent (HC) standard errors without knowledge of the functional form of conditional heteroskedasticity.
Schuler et al (NPL: Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score, 2021) teaches a method that use of historical data to exploits linear covariate adjustment to improve the efficiency of trial analyses without incurring bias. A prognostic model is trained on the historical data, then estimate the treatment effect using a linear regression while adjusting for the trial subjects’ predicted outcomes (their prognostic scores). Under certain conditions, this prognostic covariate adjustment procedure attains the minimum variance possible among a large class of estimators.
Walsh et al (NPL: Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks, 2020) teaches a Conditional Restricted Boltzmann Machine (CRBM) to learn the relationships between covariates commonly used to characterize subjects and their disease progression in MS clinical trials. A CRBM is capable of generating digital twins, which are simulated subjects having the same baseline data as actual subjects. Digital twins allow for subject-level statistical analyses of disease progression. The CRBM is trained using data from 2395 subjects enrolled in the placebo arms of clinical trials across the three primary subtypes of multiple sclerosis.
These references taken either alone or in combination with the prior art of record fail to disclose instructions, including:
Claim 4 and 14: applying parameters of the skedastic function model to a loss function for data from the target trial, to derive at least one minimizing coefficient, wherein the at least one minimizing coefficient includes a treatment effect coefficient; computing standard errors for the at least one minimizing coefficient; calculating: one or more predicted outcomes for the target trial; and one or more predicted outcomes for the historical data; and defining the skedastic function model based on: residuals corresponding to the one or more predicted outcomes for the historical data; and variances corresponding to the one or more predicted outcomes for the target trial.
in combination with the remaining elements and features of the claimed invention.
Conclusion
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/CHUEN-MEEI GAN/Primary Examiner, Art Unit 2189
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