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 .
This communication is responsive to application filed on 08/10/2023.
Claims 1-25 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/02/2024, 10/07/2025, and 05/06/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-9 are directed to a method.
Claims 10-17 are directed to a system.
Claims 18-25 are directed to a product.
Therefore, claims 1-25 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A method for estimating mechanistic chromatography model uncertainty, the method comprising:
receiving a mechanistic model of chromatography that comprises a plurality of parameters [insignificant extra solution, e.g. mere data-gathering];
identifying, for each of the plurality of parameters, a corresponding region of values based on a relationship between values for the plurality of parameters [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)];
sampling each parameter of the plurality of parameters within the corresponding region of values for each parameter to form a plurality of simulation sets [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)]; and
quantifying an uncertainty for the mechanistic model using the plurality of simulation sets [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites additional element of “receiving”. The additional element of “receiving” is insignificant pre-solution (i.e. data gathering). Accordingly, the additional element(s) of each of this claim does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): As discussed above with respect to the integration of the abstract into a practical application, the additional element of “receiving” is insignificant pre-solutions (i.e. data gathering). At most the additional element is not found to including anything more than data gathering or mere data output. See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv) - Obtaining information about transactions.
As per claims 2-7, the claims fall into [mathematical concepts].
As per claim 8, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 9, the claim falls into [insignificant extra solution, e.g. mere data-gathering and/or [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 10, independent claim 10 recites limitations analogous in scope to those of independent claim 1, and as such are similar rejected. Further, claim 10 recites additional elements of “a data source” and “a processor”. The components recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “data source” and “processor” amount to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As per Claims 11-25, claims 11-25 recite limitations analogous in scope to those of claims 1-9, and as such are similar rejected.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Briskot et al (T. Briskot, F. Stuckler, F. Wittkopp, C. Williams, J. Yang, S. Konrad, K. Doninger, J. Griesbach, M. Bennecke, S. Hepbildikler, J. Hubbuch, “Prediction uncertainty assessment of chromatography models using Bayesian inference” pgs. 101-110, 2019).
Claim 1. Briskot et al discloses a method for estimating mechanistic chromatography model uncertainty (See: Abstract, a Bayesian framework for the calibration and quality assessment of mechanistic chromatography models is introduced. Bayesian Markov Chain Monte Carlo is used to assess parameter uncertainty by generating samples from the parameter posterior distribution. Once the parameter posterior distribution has been estimated, it can be used to propagate the parameter uncertainty to model predictions, allowing a prediction-based uncertainty assessment of the model), the method comprising:
receiving a mechanistic model of chromatography that comprises a plurality of parameters (See: pg. 102 left side column, mechanistic models contain parameters and thus call upon statistics to get high confidence in these unknown model parameters. The estimation of these parameters belongs to the class of so called inverse problems; pg. 103 right side column, Parameter estimation is commonly performed from a frequentist viewpoint. In frequentist statistics the parameters are considered as fixed but unknown and the experimental data D as random samples from the true model [47]. The maximum likelihood estimate of the parameters);
identifying, for each of the plurality of parameters, a corresponding region of values based on a relationship between values for the plurality of parameters (See: pg. 105 right side column through pg. 106 left side column, In process modeling, more attention should be paid to the predictive uncertainty of the model than on interpreting the model parameters and their uncertainty. Using the generated MCMC samples, the parameter uncertainty contained in the posterior parameter distribution can be propagated accurately and easily to predictions of the mechanistic model. Given samples of the parameter posterior distribution, predictions can be made by solving the model for each of the parameter sets. The distribution of such pre dictions is referred to as the predictive posterior distribution and reflects the prediction uncertainty of the model. Fig. 1 shows the experimental fraction data used for Bayesian parameter estimation, along with the predictive posterior distribution of the model. Solid lines indicate the median of the predictive posterior distribution. The transparent areas indicate the 99 %, 95 %, and 68 % prediction intervals (PIs), respectively. It is important to note that the upper limits of the y-axes in Fig. 1 differ among the experiments);
sampling each parameter of the plurality of parameters within the corresponding region of values for each parameter to form a plurality of simulation sets (See: Abstract, Bayesian Markov Chain Monte Carlo is used to assess parameter uncertainty by generating samples from the parameter posterior distribution; pg. 103 right side column, Parameter estimation is commonly performed from a frequentist viewpoint. In frequentist statistics the parameters are considered as fixed but unknown and the experimental data D as random samples from the true model; pg. 104 right side column, The convergence of MCMC was assessed by visual inspection of the likelihood and parameter traces of the walkers. Once the Markov chain reached the region of highest posterior density, samples were acquired from the parameter posterior distribution); and
quantifying an uncertainty for the mechanistic model using the plurality of simulation sets (See: Abstract, Bayesian framework for the calibration and quality assessment of mechanistic chromatography models is introduced. Bayesian Markov Chain Monte Carlo is used to assess parameter uncertainty by generating samples from the parameter posterior distribution. Once the parameter posterior distribution has been estimated, it can be used to propagate the parameter uncertainty to model predictions, allowing a prediction-based uncertainty assessment of the model. The benefit of this uncertainty assessment is demonstrated using the example of a mechanistic model describing the separation of an antibody from its impurities on a strong cation exchanger. The mechanistic model was calibrated at moderate column load density and used to make extrapolations at high load conditions. Using the Bayesian framework, it could be shown that despite significant parameter uncertainty, the model can extrapolate beyond observed process conditions with high accuracy and is qualified to support process development).
Claim 2. Briskot et al discloses the method of claim 1, wherein identifying, for each of the plurality of parameters, the corresponding region of values comprises: computing a covariance matrix for the mechanistic model based on a selected loss function (See: pg. 102 left side column, The computation of parameter uncertainty in mechanistic chromatography models is usually based on a frequentist point of view using the Fisher information matrix (FIM) to approximate the single parameter confidence intervals and parameter covariance…. . The bootstrap is a Monte Carlo technique that uses data resampling and parameter estimation using the resampled data to determine parameter confidence intervals. Borg et al. [31,32] and Zhang et al. [33] used the para metric bootstrap to investigate the impact of measurement errors in process inputs and process outputs on model parameter uncertainty, respectively. For both approaches, the bootstrap and FIM, large parameter uncertainties have been reported in the past, but it was hardly analyzed how this parameter uncertainty affects the predictive power of the mechanistic model).
Claim 3. Briskot et al discloses the method of claim 2, wherein the selected loss function comprises at least one of a negative log-likelihood algorithm, a maximum log-likelihood algorithm, or a maximum likelihood algorithm (See: pg. 103, right side column, Parameter estimation is commonly performed from a frequentist viewpoint. In frequentist statistics the parameters are considered as fixed but unknown and the experimental data D as random samples from the true model [47]. The maximum likelihood estimate of the parameters ˆD= argmaxP D| = argmin− ln P D| (12) is obtained by maximizing the likelihood [Eq. (10)] or by min imizing the negative logarithm of the likelihood).
Claim 4. Briskot et al discloses the method of claim 2, wherein computing the covariance matrix for the mechanistic model based on the selected loss function comprises: identifying a search area using at least one of the selected loss function or another loss function (See: pg. 106 left side column, r distribution and reflects the prediction uncertainty of the model. Fig. 1 shows the experimental fraction data used for Bayesian parameter estimation, along with the predictive posterior distribution of the model. Solid lines indicate the median of the predictive posterior distribution. The transparent areas indicate the 99 %, 95 %, and 68 % prediction intervals (PIs), respectively. It is important to note that the upper limits of the y-axes in Fig. 1 differ among the experiments. In very good accordance with the experimental data, e mechanistic model describes the elution behavior of Pro2 and Pro3. Deviations from the experimental data can be observed for Pro1 at the peak maximum in Fig. 1(a) and at the peak tailing in Fig. 1(b). The sharp increase in the predicted protein concentration at about 550 ml of the linear gradient was caused by an unintended jump in the ionic strength and could also be observed in the UV trace of the ÄKTA system); and computing a local extremum for the selected loss function with respect to the search area (See: pg. 106 left side column, . Solid lines indicate the median of the predictive posterior distribution. The transparent areas indicate the 99 %, 95 %, and 68 % prediction intervals (PIs), respectively. It is important to note that the upper limits of the y-axes in Fig. 1 differ among the experiments).
Claim 5. Briskot et al discloses the method of claim 4, wherein computing the covariance matrix for the mechanistic model based on the selected loss function further comprises: computing the covariance matrix for the local extremum (See: pg. 102 left side column, The computation of parameter uncertainty in mechanistic chromatography models is usually based on a frequentist point of view using the Fisher information matrix (FIM) to approximate the single parameter confidence intervals and parameter covariance).
Claim 6. Briskot et al discloses the method of claim 1, wherein identifying, for each of the plurality of parameters, the corresponding region of values comprises: sampling the plurality of parameters to form a plurality of parameter sets (See: Abstract, a Bayesian framework for the calibration and quality assessment of mechanistic chromatography models is introduced. Bayesian Markov Chain Monte Carlo is used to assess parameter uncertainty by generating samples from the parameter posterior distribution. Once the parameter posterior distribution has been estimated, it can be used to propagate the parameter uncertainty to model predictions, allowing a prediction-based uncertainty assessment of the model); selecting an initial parameter set from the plurality of parameter sets for the mechanistic model (See: pg. 102 left side column, By repeatedly validating the model with new experimental data, it can be ensured that model assumptions are valid and accurately represent the real process. Like statistical models, mechanistic models contain parameters and thus call upon statistics to get high confidence in these unknown model parameters; pg. 103 right side column, Parameter estimation is commonly performed from a frequentist viewpoint. In frequentist statistics the parameters are considered as fixed but unknown and the experimental data D as random samples from the true model [47]. The maximum likelihood estimate of the parameters); and computing a covariance matrix for the mechanistic model based on a selected loss function that uses the initial parameter set (See: pg. 102 left side column, The computation of parameter uncertainty in mechanistic chromatography models is usually based on a frequentist point of view using the Fisher information matrix (FIM) to approximate the single parameter confidence intervals and parameter covariance).
Claim 7. Briskot et al discloses the method of claim 1, wherein quantifying the uncertainty comprises: generating a model prediction distribution for the mechanistic model using the plurality of simulation sets (See: pg. 104, right side column, To assess the propagation of parameter uncertainty to model prediction, 500 parameter sets were drawn randomly from the parameter posterior distribution. Model predictions were performed for each of the drawn parameter sets).
Claim 8. Briskot et al discloses the method of claim 7, wherein quantifying the uncertainty further comprises: identifying a confidence interval for the mechanistic model using the model prediction distribution (See: pg. 102, left side column, Another frequentist approach for the analysis of parameter uncertainty is bootstrapping. The bootstrap is a Monte Carlo technique that uses data resampling and parameter estimation using the resampled data to determine parameter confidence intervals).
Claim 9. Briskot et al discloses the method of claim 1, further comprising: receiving experiment data (See: pg. 103, right side column, Parameter estimation is commonly performed from a frequentist viewpoint. In frequentist statistics the parameters are considered as fixed but unknown and the experimental data D as random samples from the true model); and generating the mechanistic model using the experiment data (See: pg. 102, left side column, a mechanistic model is always a simplification of reality. By repeatedly validating the model with new experimental data, it can be ensured that model assumptions are valid and accurately represent the real process. Like statistical models, mechanistic models contain parameters and thus call upon statistics to get high confidence in these unknown model parameters….The limited amount of experimental data and the uncertainty in the data can lead to large parameter uncertainties or even parameter non-identifiability [27], resulting in uncertainty in model predictions… assess the prediction uncertainty of arbitrary chromatography models using Bayesian inference).
As per Claims 10-25, claims 10-25 recite limitations analogous in scope to those of claims 1-9, and as such are similar rejected.
Conclusion
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 09/14/2026
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