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 response filed 09/05/2025 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1-20 are pending and examined on the merits.
Priority
The instant application claims the benefit of priority to U.S. Provisional Application No. 63/043,310 filed on 17 June 2020. Thus, the effective filing date of the claims are 17 June 2020.
Withdrawn Rejections
No rejections withdrawn.
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 an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1, 10, and 19: “filling missing values in an input multivariate time series [] by using a temporal intensity function” provides a mathematical calculation (utilizing a mathematical function for imputation) that is considered a mathematical concept, which is an abstract idea.
“evaluating treatments, via the DDGM, for the dialysis patients based on the input multivariate time series with filled missing values using correlations between different time series samples" provides mathematical calculations (running a model involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
"selecting an optimized treatment, via the DDGM, for the dialysis patients" provides an evaluation (making a selection based on data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claims 2 and 11: “the temporal intensity function models temporal relationships between time steps” provides a mathematical relationship (modeling relationships over time) that is considered a mathematical concept, which is an abstract idea.
Claims 4 and 13: “captures correlations between different dimensions of the input multivariate time series” provides a mathematical calculation (correlation analysis) that is considered a mathematical concept, which is an abstract idea.
Claims 8, 17, and 20: “the inferred latent variables are provided to the generative network to generate another copy of cluster variables” provides a mathematical calculation (cluster analysis) that is considered a mathematical concept, which is an abstract idea.
Claims 9 and 18: “the generative network uses the generated cluster variables as its own input to iteratively generate new cluster variables for time steps after T” provides a mathematical calculation (cluster analysis) that is considered a mathematical concept, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while some of the claims (13, 14, 15, and 18) recite performing some aspects of the analysis with a “computing device”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
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). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claims 1, 10, and 19: “storing, via a forecasting component, parameters that represent cluster centroids” provides insignificant extra-solution activities (storing data are post-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“the correlation parameters being dynamically adjusted at each time step" provides insignificant extra-solution activities (adjusting parameters is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
The steps for storing data and adjusting parameters are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering and manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
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 claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “implementing program instructions” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for storing data and adjusting parameters are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional.
The additional elements do not comprise an inventive concept when considered individually or
as an ordered combination that transforms the claimed judicial exception into a patent -eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 9/5/2025 are fully considered but they are not persuasive.
Applicant asserts that the amendments to claims 1, 10, and 19 now recite patent eligible subject matter in the form of a practical application (Remarks 09/05/2025 Page 2). All other claims depend from independent claims 1, 10, and 19, therefore are likewise patentable under 35 U.S.C. 101. The Examiner has indicated in amended section "Claim Rejections - 35 USC § 101" above that these amendments provide mathematical concepts and mental processes which are both abstract ideas. Additionally, the amended additional element of adjusting parameters is an insignificant extra-solution activity that does not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by this amendment as parameter adjustments are well-understood, routine, and conventional.
The Examiner also notes that MPEP 2106(I) states that if the claims are directed to a judicial exception, the second part of the Mayo test is to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. Id. citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). In the “search for an ‘inventive concept’” (the second part of the Alice/Mayo test), the additional elements identified do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception because adjusting parameters and storing data (data gathering and manipulation steps) are all well-understood, routine, and conventional techniques that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Therefore, combining insignificant extra-solution activities with any of the identified judicial exceptions would not result in patent eligible subject matter because integrating well-understood, routine, and conventional techniques does not yield “significantly more” to a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon.
Therefore, the rejection of claims 1, 10, and 19 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
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.
Claims 1, 2, 4, 10, 11, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over
Bottinger et al. (US-20170228507) in view of Ylipaavalniemi et al. (US-20160063397), Silva et al. (Silva et al., "Multivariate data imputation using Gaussian mixture models", Spatial Statistics, Volume 27, 2018, Pages 74-90, ISSN 2211-6753, https://doi.org/10.1016/j.spasta.2016.11.002), Bequette et al. (Bequette et al. "Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms." Journal of diabetes science and technology 4.2 (2010): 404-418), and Lunze et al. (Lunze et al. "Blood glucose control algorithms for type 1 diabetic patients: A methodological review." Biomedical signal processing and control 8.2 (2013): 107-119).
Regarding independent claims 1, 10, and 19, Bottinger teaches:
a method for managing dialysis patient data (Para.0081 "though we used CKD [chronic kidney disease] as an example the opportunities for examining distinct disease progression subtypes and making innovative discoveries are endless in any disease area depending on available data in the EMR [electronic medical record]");
forecasting medical time series data (Para.0006 " an example method of automated medical diagnosis includes obtaining an electronic longitudinal data set for each of a plurality of patients, where each data set includes a plurality of measurement values corresponding to a metric, where each measurement value is associated with a respective time point" and Para.0039 "Further, as each patient in the cluster may [be] at a different point in disease progression, this technique can be used to predict each patient's present disease progression and to estimate their future disease progression.");
a Gaussian mixture model (Para.0042 "This clustering model can be based on a multivariate mixture of Gaussians"); and
storing model parameters that represent cluster centroids used by the model to cluster time series for capturing correlations between different time series samples (Para.0082 "For example, in some implementations, medical [data] (e.g., EMRs) can be stored, maintained, revised, and/or retrieved using a system implemented using digital electronic circuitry, or in computer software, firmware, or hardware, or in combinations of one or more of them.",Claim 3 "each cluster center comprises a plurality of reference values, each measurement value associated with a respective reference time point", and (Claim 1 "A method of automated medical diagnosis, the method comprising determining a similarity between each data set and each cluster center").
Bottinger does not explicitly teach a deep learning method for predicting future states, or filling missing values in an input multivariate time series using imputation, correlation parameters being dynamically adjusted at each time step, nor evaluating treatments based on multivariate time series with filled missing values using correlations between different time series samples; and selecting an optimized treatment, via the DDGM, for the dialysis patients.
However, Ylipaavalniemi teaches a deep learning method for predicting future states (Para.0166 "based on the performance model weights and the condition inputs, a state-space model may be hierarchically constructed using suitable algorithms, e.g. Hierarchical Clustering, Support Vector Machine (SVM), Neural Networks (ANN) and/or Deep Learning" and Para.0169 "Predictive models may be time-series models and/or predicting transition probability models.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to combine the GMM of Bottinger with a hierarchically built deep learning model for predicting future states as taught by Ylipaavalniemi in order to vary the level of detail of the underlying system (Para.0166 "Constructing the state-space model hierarchically has the advantage that the state of the underlying biometric system may be defined in various levels of detail"). One skilled in the art would have a reasonable expectation of success because of the complementary strengths of a GMM and deep learning model (GMM utilizing probabilistic clustering and deep learning for feature extraction).
However, Silva teaches filling missing values in an input multivariate time series using Gaussian kernels and multi-dimensional correlation (Abstract "The missing data must be imputed (inferred) to permit the measured data to be used to their full extent. Imputation methods for geological data should address spatial structure and multivariate complexity.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Bottinger as taught by Silva in order to provide stability in fitting multivariate data and improve computational efficiency (Para.0304 "A Gaussian mixture model fitted to the multivariate data is proposed in this paper to provide stability in fitting multivariate data and to significantly improve computational efficiency."). One skilled in the art would have a reasonable expectation of success because both approaches are using time series data.
However, Bequette teaches the correlation parameters being dynamically adjusted at each time step (page 2 col 2 paragraph 5 "The correlation coefficient is a measure of the quality of the model fit; if the correlation coefficient is too low, the calibration may be deemed unacceptable, requiring additional reference glucose measurements, as discussed in the patent by Goode and colleagues.16 Further, the patent by Feldman and McGarraugh17 discussed criteria for calibration acceptance").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Bottinger as taught by Bequette in order to reduce mean glucose levels and avoid risk of hypoglycemia (page 1 col 1 paragraph 1 "continuous glucose monitoring(CGM) has the potential to further reduce mean glucose levels while avoiding the risk of hypoglycemia"). One skilled in the art would have a reasonable expectation of success because both approaches are using longitudinal data for predicting a patients disease progression.
However, Lunze teaches evaluating treatments based on multivariate time series with filled missing values using correlations between different time series samples; and selecting an optimized treatment, via the DDGM, for the dialysis patients:
Page 2 col 2 paragraph 2 "The basic idea is to calculate the required insulin dose using a control algorithm based on continuous glucose measurements, which are obtained via a sensor without human input" demonstrates evaluating treatments (insulin doses).
Page 7 col 2 section 5.2.1 paragraph 1 "Hence, for the case of state space representation, if it is not possible to measure the entire system state, a state estimator has to be included to estimate the unmeasured states" demonstrates filling missing values.
Page 7 col 1 paragraph 1 "The repetitive blood glucose control strategy focuses on the adaptation of the basal insulin infusion rate. It cannot respond to a rapid change in glucose concentration which may cause severe hyperglycemia. Therefore, the control method should be advanced such that it calculates continuous time-dependent insulin infusion doses and counteracts extreme blood glucose variations" demonstrates explicitly modeling temporal dependence which implies using correlations between different time series samples.
Page 6 col 2 section 4.2.3 paragraph 1 "As an alternative method adapted from the chemical process industry [55], Palerm et al. applied the run-to-run optimization strategy to blood glucose control to find the best-fitting basal insulin injection doses [52]" demonstrates finding an optimal treatment strategy.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Bottinger and various machine learning implementations of Ylipaavalniemi as taught by Lunze in order to have a flexible enough control algorithm for high performance insulin control requirements (Lunze page 3 col 2 paragraph 3 "The time delays associated with subcutaneous glucose sensing and insulin injection are a serious problem for control design. Therefore, advanced control algorithms based on patient models are needed which, in turn, require patient information in order to adapt control performance to system changes. Applying a nonlinear assessment technique on the glucose control problem, Hernjak and Doyle concluded that a linear internal model is sufficient for glucose control, but that a simple PD control algorithm is not flexible enough for high performance requirements [23]"). One skilled in the art would have a reasonable expectation of success because both approaches are using longitudinal data for disease prediction modeling.
Regarding claims 2 and 11, Bottinger in view of Ylipaavalniemi and Silva teach the methods of Claims 1 and 10 on which these claims depend. Bottinger also teaches the temporal intensity function models temporal relationships between time steps (Para.0039 "Further, as each patient in the cluster may [be] at a different point in disease progression, this technique can be used to predict each patient's present disease progression and to estimate their future disease progression").
Regarding claims 4 and 13, Bottinger in view of Ylipaavalniemi and Silva teach the methods of Claims 1 and 10 on which these claims depend. Bottinger also teaches the multi-dimensional correlation captures correlations between different dimensions of the input multivariate time series (Claim 1 "A method of automated medical diagnosis, the method comprising [] determining a similarity between each data set and each cluster center").
Claims 3, 5, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bottinger et al. (US-20170228507) in view of Ylipaavalniemi et al. (US-20160063397), Silva et al. (Silva et al., "Multivariate data imputation using Gaussian mixture models", Spatial Statistics, Volume 27, 2018, Pages 74-90, ISSN 2211-6753, https://doi.org/10.1016/j.spasta.2016.11.002), Bequette et al. (Bequette et al. "Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms." Journal of diabetes science and technology 4.2 (2010): 404-418), and Lunze et al. (Lunze et al. "Blood glucose control algorithms for type 1 diabetic patients: A methodological review." Biomedical signal processing and control 8.2 (2013): 107-119) as applied to claims 1, 2, 4, 10, 11, 13, and 19 above, and further in view of Hu et al. (Hu et al., "Clinical decision support for Alzheimer's disease based on deep learning and brain network," 2016 IEEE International Conference on Communications (ICC), pp. 1-6, doi: 10.1109/ICC.2016.7510831).
Bottinger et al. in view of Ylipaavalniemi et al., Silva et al., Bequette et al., and Lunze et al. are applied to claims 1, 2, 4, 10, 11, 13, and 19.
Regarding claims 3 and 12, Bottinger in view of Ylipaavalniemi, Silva, Bequette, and Lunze teach the method of Claims 2 and 11 on which these claims depend.
Bottinger, Ylipaavalniemi, nor Silva explicitly teach the temporal intensity function is based on an inverse distance weighting mechanism.
However, Hu teaches calculating a modified inverse Hessian matrix which may utilize inverse distance weighting that will ensure that closer points have more influence than distant ones (Page 3 col 1 first paragraph "However, quasi-Newton method gets over the situation by calculating the approximate inverse Hessian matrix.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Bottinger, Ylipaavalniemi, and Silva as taught by Hu in order to reduce the memory footprint of the model (Page 3 col 1 first paragraph "L-BFGS [limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm], as the improvement of BFGS, takes less resource of memory than BFGS, so it is widely used in practice. Instead of fully calculating and restoring the inverse Hessian matrix, L-BFGS merely uses the least vectors to represent a matrix."). One skilled in the art would have a reasonable expectation of success because this modified method has been demonstrated successfully in a clinical decision support algorithm similar to the aims of the algorithm in the instant application.
Regarding claims 5 and 14, Bottinger in view of Ylipaavalniemi and Silva teach the method of Claims 4 and 13 on which these claims depend.
Bottinger, Ylipaavalniemi, nor Silva explicitly teach the multi-dimensional correlation initializes a matrix parameter ρ ∈ RD×D, which is a D by D continuous matrix and each entry pij represents the correlation between dimension i and j.
However, Hu teaches a correlation matrix obtained by calculating correlations between pairs of data (Abstract "Secondly, to represent the functional connectivity between different brain regions, a correlation matrix is obtained by calculating the correlation between each pair of brain regions").
Claims 6-9, 15-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bottinger et al. (US-20170228507) in view of Ylipaavalniemi et al. (US-20160063397), Silva et al. (Silva et al., "Multivariate data imputation using Gaussian mixture models", Spatial Statistics, Volume 27, 2018, Pages 74-90, ISSN 2211-6753, https://doi.org/10.1016/j.spasta.2016.11.002), Bequette et al. (Bequette et al. "Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms." Journal of diabetes science and technology 4.2 (2010): 404-418), and Lunze et al. (Lunze et al. "Blood glucose control algorithms for type 1 diabetic patients: A methodological review." Biomedical signal processing and control 8.2 (2013): 107-119) as applied to claims 1, 2, 4, 10, 11, 13, and 19 above, and further in view of Bengio et al. (Bengio et al., "Towards biologically plausible deep learning." arXiv preprint arXiv:1502.04156 (2015).).
Bottinger et al. in view of Ylipaavalniemi et al., Silva et al., Bequette et al., and Lunze et al. are applied to claims 1, 2, 4, 10, 11, 13, and 19.
Regarding claims 6-9, 15-28, and 20, Bottinger in view of Ylipaavalniemi, Silva, Bequette, and Lunze teach the method of Claims 1, 10, and 19 on which these claims depend.
Bottinger, Ylipaavalniemi, nor Silva explicitly teach: the forecasting component includes an inference network and a generative network; the inference network infers latent variables; the inferred latent variables are provided to the generative network to generate another copy of cluster variables; nor after time T, the generative network uses the generated cluster variables as its own input to iteratively generate new cluster variables for time steps after T, which are integral steps for the training of recurrent neural networks.
However, Bengio teaches an iterative inference network that infers latent variables that are provided as input to other layers of the network (Parge 1 col 1 last paragraph "We first argue that the above interpretation of STDP [spike timing-dependent plasticity] suggests that neural dynamics (which creates the above changes in neuronal activations thanks to feedback and lateral connections) correspond to inference towards neural configurations that are more consistent with each other and with the observations (inputs, targets, or rewards)" and Page 2 col 1 last paragraph "In Sec. 4 we show how this mathematical framework suggests a training procedure for a deep directed generative network with many layers of latent variables.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Bottinger, Ylipaavalniemi, and Silva as taught by Bengio in order to improve sampling for model training (Page 2 col 2 paragraph 1 "We introduce a novel justification for difference target propagation (Lee et al., 2014), exploiting the fact that the proposed learning mechanism can be interpreted as training a denoising auto-encoder. As discussed in Sec. 5 these alternative interpretations of the model provide different ways to sample from it, and we found that better samples could be obtained"). One skilled in the art would have a reasonable expectation of success because both Bengio et al. and the instant application are using deep learning methods.
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 9/5/2025 are fully considered but they are not persuasive.
Regarding Claims 1, 10, and 19, Applicant argues that Bottinger fails to teach the amended limitation "evaluating treatments", or actually use or apply the data outside the alleged abstract idea (i.e. does not act on the diagnosis) (Remarks 09/05/2025 Pages 4 and 7-8). Applicant also asserts that Ylipaavalniemi "does not evaluate multiple options at once, and at most authenticates a subject when a match is found", and "updates the model by adding and removing elements from the state-space rather than altering correlation parameters" (Remarks 09/05/2025 Pages 4 and 8-9). Applicant also asserts that Silva, like Bottinger, "does not actually teach performing any function with the data or dynamically changing the parameters" (Remarks 09/05/2025 Pages 5 and 9). Additionally, Applicant argues that Bottinger, Ylipaavalniemi, and Silva fail to teach or suggest the amended limitation "the correlation parameters being dynamically adjusted at each time step" (Remarks 09/05/2025 Pages 4-7). Applicant concedes that Ylipaavalniemi does teach updating the model, but "one of ordinary skill in the art would not be taught to include 'correlation parameters being dynamically adjusted at each time step' from altering parameters at iterations or removing or adding elements from the state-space vectors". Finally, Applicant argues that all other claims depend from independent claims 1, 10, and 19, therefore are also rendered patentable under 35 U.S.C. 103.
The Examiner has indicated above, that with respect to the arguments surrounding the prior art not teaching the amendments to claims 1, 10, and 19, Bequette teaches the correlation parameters being dynamically adjusted at each time step, and Lunze demonstrates evaluating treatments (insulin doses), filling missing values, explicitly modeling temporal dependence which implies using correlations between different time series samples, and finding an optimal treatment strategy. Please refer to amended section "Claim Rejections - 35 USC § 103" above, for details and citations. Additionally, with respect to Applicant's argument regarding prior art not having an algorithm perform the selecting step (Remarks 09/05/2025 Pages 4-7), Lunze also teaches and addresses this aspect and significantly more (page 2 col 2 paragraph 2 "The basic idea is to calculate the required insulin dose using a control algorithm based on continuous glucose measurements, which are obtained via a sensor without human input. For this, a mathematical patient model may support the computation of an appropriate insulin injection. Then, the precise insulin dose is automatically administered via a pump that continuously delivers insulin. Fig. 2 shows a schematic of the resulting closed loop system in which the patient appears only once, i.e. as the glucose metabolic system to be controlled").
Therefore, the rejection of claims 1, 10, and 19 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise 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 TH REE-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 finaI action.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686