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 .
Claim Status
Claims 1, 3, 5, 6, 12-14, 16, and 18-20 were amended by Applicant’s paper filed 27 February 2026 (hereinafter “Amendment”).
Claims 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Priority
The instant application claims benefit to provisional application No. 63/194,656 filed on 28 May 2021. Domestic benefit is acknowledged. At this point in examination, the effective filing date of claims 1-20 is 28 May 2021.
Response to Amendment - 35 USC § 112
Applicant’s arguments, see p. 10 para. 6 – p. 11 para. 5, filed 27 February 2026, with respect to claims 1-20 have been fully considered and are persuasive. The 112(b) rejection of claims 1-20 has been withdrawn.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Irizarry (57(45) Ind. Eng. Chem. Res. 15329-45 (October 17, 2018)), Irizarry PG-PUB (US 20190371436 A1 published 5 December 2019), and Yeom (11(6) Pharmaceutics 264 (6 June 2019)), as evidenced by Azlin (RSC Adv., Vol. 5 (March 18, 2015)) and Maria (Eur. Polym. J. Vol. 234 (June 23, 2025)). The italicized text corresponds to the instant claim limitations.
Regarding claim 1, Irizarry discloses a model to predict the release profile of a drug distributed in a polymer matrix based on imaging data [Abstract] (determining a model predicting a release profile of the substance from the material matrix; wherein the release system comprises the material matrix and the substance dispersed in the material matrix). Irizarry teaches that after imaging the device and analyzing the images, a model to predict the release profile of the device is constructed based on the imaging data of the device. [p.15330/col.2; Minimalistic Model Construction p.15335] (constructing a simulation model of a release system based on imaging data of the release system). Irizarry then determines the model to predict the release profile by approximating parameters of the model affecting effective diffusivity based on the image-extracted data of the device [p.15338/col.1/par.1] (modifying parameters of the constructed simulation model based upon the release system characteristic data to correct a transport coefficient of the release system; instant specification P0049, “[e]xample transport coefficients are effective diffusivity coefficient and permeability”). The constructed model is employed with the approximated parameters to predict a release coefficient, which is utilized to measure the release profile of a device [p.15339/col. 1] (performing a simulation of the release system using the constructed simulation model with the modified parameters to generate a simulation-based release profile). The parameter values that produce a model with the lowest squared error between the model and experimental release data are selected as the optimal parameters for the model predicting the release profile [p.15339/col. 1]. Irizarry discloses that the results of the model can be calibrated with historical release data to generate anticipated release curves for new formulation batches [p.15341/col.1/para/2] (setting the constructed simulation model with the modified parameters used in performing the simulation to generate the given simulation-based release profile, as the model predicting the release profile of the substance from the material matrix).
This disclosure of Irizarry does not explicitly teach the method being computer-implemented or the process of modifying parameters and performing a simulation being iterative. However, Irizarry PG-PUB explicitly discloses that the method can be computer-implemented [Figure 9, computer 900]. Irizarry PG-PUB also teaches that the calibration with historical release data discussed in Irizarry may be performed using an iterative solver [P0061/L9-13]. Moreover, Yeom discloses a discrete element method (DEM) model to aid in the scale-up strategy of the pharmaceutical manufacturing process [p.2/para.4]. Yeom develops the DEM model by defining input parameters, such as the material properties and interaction parameters of each material [p.2/para.4]. Yeom notes difficulty in defining or directly measuring interaction parameters, and implements an indirect method of calibration based on comparison between the simulated and the experimental values [p.6/para.7]. Yeom teaches replicating an experiment in the DEM model and iteratively changing the input parameters until the simulation results converge on the experimental results [p.7/para.1] (until a given simulation-based release profile matches release system characteristic data). After calibration, Yeom discloses using the calibrated DEM model to simulate the pilot-scale blending process to evaluate the change of operating space during scale-up [p.25/para.2] (setting the constructed simulation model with the modified parameters used in performing the simulation to generate the given simulation-based release profile, as the model predicting the release profile of the substance from the material matrix).
Irizarry teaches a base method of determining a model for predicting the release profile of a controlled release device based on imaging data to generate a predictive model that estimates the release profile of the device. Irizarry discloses calibrating the model using experimental release data to improve prediction accuracy, but describes the calibration process at a high level and does not detail a specific procedure for repeatedly refining the model parameters. Irizarry PG-PUB discloses that calibration based on experimental data can be performed using an iterative solver [P0061/L9-13]. Yeom teaches a known calibration technique in pharmaceutical simulation modeling of iteratively modifying input parameters, running a simulation, comparing the simulation outputs to experimental data, and repeating the process until the simulation results are in agreement with the experimental results [p.15/para.3]. A person having ordinary skill in the art would recognize that applying the known calibration technique of Yeom to Irizarry’s method of determining a model based on imaging data would predictably yield an improved method of determining a release profile by providing a more accurate predictive model in which the transport coefficient is properly corrected such that the simulation-based release profile matches the experimental characteristic data. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the method of Irizarry by calibrating the model using an iterative process as taught by Yeom and suggested by Irizarry PG-PUB. Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, D.
Regarding claim 2, Irizarry discloses determining the model to predict the release profile by approximating parameters of the model affecting effective diffusivity based on the image-extracted data of the device [p.15338/col.1] (modifying parameters of the constructed simulation model affecting the transport coefficient; Specification P0049, “[e]xample transport coefficients are effective diffusivity coefficient and permeability”).
Regarding claim 3, Irizarry PG-PUB explicitly discloses that, assuming a consistent microstructure, imaging can be performed on only a fraction of the release device [P0066/L1-4] (the imaging data is of a representative sample of the release system). Irizarry PG-PUB also discloses that the model may be based on imaging data of the release device and experimentally determined release data [P0061/L3-8] (wherein the constructed simulation model is constructed based on both the imaging data of the representative sample of the release system and known parameters approximating a release mechanism of the release system).
Regarding claim 4, Irizarry teaches that, after construction, the model is calibrated using experimental release data to account for unresolved features that can cause variation in the effective diffusivity [p.15333/col.2; p.15334/col.2]. Irizarry teaches that calibration of the model allows for a more accurate prediction of the release profile by accounting for the difference between actual diffusivity and the initial model-predicted diffusivity [p.15341]. Yeom teaches a known calibration technique in pharmaceutical simulation modeling of iteratively modifying input parameters, running a simulation, comparing the simulation outputs to experimental data, and repeating the process until the simulation results are in agreement with the experimental results [p.15/para.3] (creating a first modified model of the release system by modifying, in accordance with the release system characteristic data, given parameters of the constructed simulation model to modify the transport coefficient due to a difference between actual diffusivity of the substance and diffusivity in the given parameters). Irizarry discloses that when the calibrated model is simulated to predict the release profile of a device, it is accurate with less than 10% prediction discrepancies against experimental data [p.15341/col. 1] (performing a simulation using the first modified model to determine a first simulation-based release profile, wherein the first simulation-based release profile is the given simulation-based release profile that matches the release system characteristic data).
Regarding claim 5, Irizarry teaches that, after construction, the model is calibrated using experimental release data to account for unresolved features that can cause variation in the effective diffusivity [p.15333/col.2; p.15334/col.2] (determining the model predicting the release profile of the substance from the material matrix). Irizarry teaches that calibration of the model allows for a more accurate prediction of the release profile by accounting for the difference between actual diffusivity and the initial model-predicted diffusivity [p.15341] (creating a first modified model of the release system by modifying, in accordance with the release system characteristic data, given parameters of the constructed simulation model to modify the transport coefficient due to a difference between actual diffusivity of the substance and diffusivity in the given parameters). Yeom teaches a known calibration technique in pharmaceutical simulation modeling of iteratively modifying input parameters, running a simulation, comparing the simulation outputs to experimental data, and repeating the process until the simulation results are in agreement with the experimental results [p.15/para.3] (in a first iteration; performing a simulation using the first modified model to determine a first simulation-based release profile, wherein the first simulation-based release profile does not match the release system characteristic data; in a second iteration: creating a second modified model of the release system by modifying, in accordance with the release system characteristic data, given parameters of the first modified model to correct changes to the transport coefficient due to variability in diffusivity of the substance over time caused by changes of the material matrix during the release). Irizarry discloses that when the calibrated model is simulated to predict the release profile of a device, it is accurate with less than 10% prediction discrepancies against experimental data [p.15341/col.1] (performing a simulation using the second modified model to determine a second simulation-based release profile, wherein the second simulation-based release profile is the given simulation-based release profile matching the release system characteristic data). Irizarry discloses that the aim of the model is to account for variations in the diffusivity coefficient caused by changes to the release device structure during the release [p.15333/col.2] (to correct changes to the transport coefficient due to variability in diffusivity of the substance over time caused by changes of the material matrix during the release).
Regarding claims 6 and 19, Irizarry PG-PUB discloses that the constructed model may be based on imaging data of the release device and experimentally determined release data [P0061/L3-8] (where the constructed simulation model is constructed based upon both the imaging data and known parameters that approximate a release mechanism of the release system). Irizarry teaches calibrating the model using experimental release data to account for unresolved features that can cause variation in the effective diffusivity [p.15333/col.2; p.15334/col.2] (determining the model predicting the release profile of the substance from the material matrix; modifying the parameters of the constructed simulation model affecting the transport coefficient; instant specification P0049, “[e]xample transport coefficients are effective diffusivity coefficient and permeability”). Irizarry discloses that when the calibrated model is simulated to predict the release profile of a device, it is accurate with less than 10% prediction discrepancies against experimental data [p.15341/col.1] (until the given simulation-based release profile matches).
Yeom discloses conducting 15 experiments on the blending process [p.4/para.1] (the release system characteristic data comprises in vitro release system characteristic data). Yeom teaches replicating the experiments in the DEM model and iteratively changing the input parameters until the simulation results converge on the experimental results [p.7/para.1] (until the given simulation-based release profile matches the in vitro release system characteristic data, iteratively (i) modifying the parameters of the constructed simulation model affecting the transport coefficient in accordance with the in vitro release system characteristic data and (ii) performing a simulation of the release system using the constructed simulation model with the modified parameters to generate a simulation-based release profile). Irizarry PG-PUB explicitly discloses a computer system comprising a processor and a memory with stored code instructions configured to cause the system to execute this method [P0071-72; Figure 9, processor 902, memory 906] (the processor and the memory, with the computer code instructions, are further configured to cause the system to).
Regarding claim 7, Irizarry discloses that the parameter values that produce a model with the lowest squared error between the model and experimental release data are selected as the optimal parameters for the model predicting the release profile [p.15339/col. 1] (determining the given simulation-based release profile matches the release system characteristic data by performing a least squares fitting between the given simulation-based release profile and a release profile indicated by the release system characteristic data).
Regarding claim 8, Irizarry teaches determining pre-existing porosity based on the images of the release device and approximating parameters of the model based on the pre-existing porosity [p.15331/col.1/par.4; p.15342/col.2/par.2] (wherein the release system characteristic data includes at least one of: … an indication of pre-existing porosity). Irizarry also teaches an idealized geometry of the release system being cylindrical [p.15335/col.2/par.3] (wherein the release system characteristic data includes at least one of: … an indication of geometry of the release system being cylindrical, spherical, or plate).
Regarding claim 9, Irizarry discloses the release device comprising an active pharmaceutical ingredient dispersed in a polymer matrix [p.15329/col.1] (wherein the substance is at least one of: a pharmaceutical ingredient; an insecticide; and an anti-corrosion agent).
Regarding claim 10, Irizarry discloses the release device comprising an active pharmaceutical ingredient dispersed in a polymer matrix [p.15329/col.1] (wherein the material matrix is a polymer).
Regarding claim 11, Irizarry PG-PUB explicitly discloses that “the polymer matrix may be ethylene vinyl acetate (EVA), polyurethane, polylactic acid (PLA), polycaprolactone (PCL), or the like” [P0034/L5-7]. Ethyl vinyl acetate is known to be biostable as evidenced by Azlin [p.31486/col.1/par.3] and polycaprolactone is known to be biodegradable as evidenced by Maria [p.2/col.1/par.3] (wherein the polymer is either biostable or biodegradable).
Regarding claim 12, Irizarry teaches analyzing the images of the release device to extract key features such as pore size distribution, pore connectivity, and coordination number which are used as model inputs to predict drug release [p.15334/col.1] (determining at least one image-derived release system characteristic from the imaging data; and updating a parameter of the simulation model of the release system to correspond to the determined at least one image-derived release system characteristic).
Regarding claim 13, Irizarry PG-PUB discloses measuring characteristics of active compound domains during image analysis, including the size, location, and amount of the substance to be released [P0034] (the at least one image-derived release system characteristic includes at least one of: size of the substance, amount of the substance, location of the substance in the release system). Irizarry PG-PUB also discloses image-extracted data comprising pore size and pore location [P0050] (pore size of the material matrix, location of pores). Irizarry discloses utilizing images to assess porosity of the material matrix [p.15331/col.1/par.4] (porosity of the material matrix).
Regarding claim 14, Irizarry teaches that the model can account for diffusivity over time relating to changes in the release device structure [p.15333/col.2] (wherein the transport coefficient indicates at least one of: … diffusivity of the substance over time caused by changes of the release system during release).
Regarding claim 15, Irizarry discloses accounting for aggregates within the system [p.15334/col.1/par.2]. Irizarry PG-PUB clarifies that it is the aggregates of the active compound that must be accounted for due to the effect on the release profile of the system [P0034/L16-21] (wherein the changes of the release system include at least one of: … aggregation of the substance).
Regarding claim 16, Irizarry discloses a model to predict the release profile of a drug distributed in a polymer matrix based on imaging data [Abstract] (determining a model predicting a release profile of a substance from a material matrix; wherein the release system comprises the material matrix and the substance dispersed in the material matrix). Irizarry PG-PUB explicitly discloses this method being implemented into a computer system comprising a processor and a memory with stored code instructions [P0071-72; Figure 9, processor 902, memory 906] (computer system comprising: a processor; and a memory with computer code instructions stored thereon). Irizarry teaches that after imaging the device and analyzing the images, a model to predict the release profile of the device is constructed based on the imaging data of the device. [p.15330/col.2/par.5; p15335] (construct a simulation model of a release system based on imaging data of the release system). Irizarry then determines the model to predict the release profile by approximating parameters of the model affecting effective diffusivity based on the image-extracted data of the device [p.15338] (modifying parameters of the constructed simulation model based upon the release system characteristic data to correct a transport coefficient of the release system; instant specification P0049, “[e]xample transport coefficients are effective diffusivity coefficient and permeability”). The constructed model is employed with the approximated parameters to predict a release coefficient, which is utilized to measure the release profile of a device [p.15339/col.1] (performing a simulation of the release system using the constructed simulation model with the modified parameters to generate a simulation-based release profile). The parameter values that produce a model with the lowest squared error between the model and experimental release data are selected as the optimal parameters for the model predicting the release profile [p.15339/col.1]. Irizarry discloses that the results of the model can be calibrated with historical release data to generate anticipated release curves for new formulation batches [p.15341/col.1/para/2] (setting the constructed simulation model with the modified parameters used in performing the simulation to generate the given simulation-based release profile, as the model predicting the release profile of the substance from the material matrix). Yeom teaches calibrating a model by iteratively changing the input parameters until the simulation results converge on the experimental results [p.7/para.1] (until a given simulation-based release profile matches release system characteristic data, iteratively: (i) modifying parameters … and (ii) performing a simulation).
Regarding claim 17, Irizarry teaches that, after construction, the model is calibrated using experimental release data to account for unresolved features that can cause variation in the effective diffusivity [p.15333/col.2; p.15334/col.2] (determining the model predicting the release profile of the substance from the material matrix). Irizarry teaches that calibration of the model allows for a more accurate prediction of the release profile by accounting for the difference between actual diffusivity and the initial model-predicted diffusivity [p.15341] (create a first modified model of the release system by modifying, in accordance with the release system characteristic data, given parameters of the constructed simulation model to modify the transport coefficient due to a difference between actual diffusivity of the substance and diffusivity in the given parameters). Yeom teaches a known calibration technique in pharmaceutical simulation modeling of iteratively modifying input parameters, running a simulation, comparing the simulation outputs to experimental data, and repeating the process until the simulation results are in agreement with the experimental results [p.15/para.3]. Irizarry discloses that when the calibrated model is simulated to predict the release profile of a device, it is accurate with less than 10% prediction discrepancies against experimental data [p.15341/col.1] (perform a simulation using the first modified model to determine a first simulation-based release profile, wherein the first simulation-based release profile is the given simulation-based release profile that matches the release system characteristic data). Irizarry PG-PUB discloses this method effectuated by a processor and a memory with stored code information [P0071-72; P0079-80] (the processor and the memory, with the computer code instructions, are further configured to cause the system to).
Regarding claim 18, Irizarry teaches that, after construction, the model is calibrated using experimental release data to account for unresolved features that can cause variation in the effective diffusivity [p.15333/col.2; p.15334/col.2] (determining the model predicting the release profile of the substance from the material matrix). Irizarry teaches that calibration of the model allows for a more accurate prediction of the release profile by accounting for the difference between actual diffusivity and the initial model-predicted diffusivity [p.15341] (create a first modified model of the release system by modifying, in accordance with the release system characteristic data, given parameters of the constructed simulation model to modify the transport coefficient due to a difference between actual diffusivity of the substance and diffusivity in the given parameters). Yeom teaches a known calibration technique in pharmaceutical simulation modeling of iteratively modifying input parameters, running a simulation, comparing the simulation outputs to experimental data, and repeating the process until the simulation results are in agreement with the experimental results [p.15/para.3] (in a first iteration; perform a simulation using the first modified model to determine a first simulation-based release profile, wherein the first simulation-based release profile does not match the release system characteristic data; in a second iteration: create a second modified model of the release system by modifying, in accordance with the release system characteristic data, given parameters of the first modified model to correct changes to the transport coefficient). Irizarry discloses that when the calibrated model is simulated to predict the release profile of a device, it is accurate with less than 10% prediction discrepancies against experimental data [p.15341/col.1] (perform a simulation using the second modified model to determine a first simulation-based release profile; wherein the second simulation-based release profile is the given simulation-based release profile matching the release system characteristic data). Irizarry discloses that the aim of the model is to account for variations in the diffusivity coefficient caused by changes to the release device structure during the release [p.15333/col.2] (to correct changes to the transport coefficient due to variability in diffusivity of the substance over time caused by changes of the material matrix during release). Irizarry PG-PUB explicitly discloses a computer system comprising a processor and a memory with stored code instructions configured to cause the system to execute this method [P0071-72; Figure 9, processor 902, memory 906] (the processor and the memory, with the computer code instructions, are further configured to cause the system to).
Regarding claim 20, Irizarry discloses a model to predict the release profile of a drug distributed in a polymer matrix based on imaging data [Abstract] (determining a model predicting a release profile of a substance from a material matrix; wherein the release system comprises the material matrix and the substance dispersed in the material matrix). Irizarry PG-PUB explicitly discloses a computer program product comprising a tangible non-transitory computer-readable storage medium storing code information to be executed by a processor [P0072; P0081; Figure 9, processor 902, memory 906] (computer system comprising: a processor; and a memory with computer code instructions stored thereon). Irizarry teaches that after imaging the device and analyzing the images, a model to predict the release profile of the device is constructed based on the imaging data of the device. [p.15330/col.2; p.15335] (construct a simulation model of a release system based on imaging data of the release system). Irizarry then determines the model to predict the release profile by approximating parameters of the model affecting effective diffusivity based on the image-extracted data of the device [p.15338] (modifying parameters of the constructed simulation model based upon the release system characteristic data to correct a transport coefficient of the release system; instant specification P0049, “[e]xample transport coefficients are effective diffusivity coefficient and permeability”). The constructed model is employed with the approximated parameters to predict a release coefficient, which is utilized to measure the release profile of a device [p.15339/col.1] (performing a simulation of the release system using the constructed simulation model with the modified parameters to generate a simulation-based release profile). The parameter values that produce a model with the lowest squared error between the model and experimental release data are selected as the optimal parameters for the model predicting the release profile [p.15339/col.1] Irizarry discloses that the results of the model can be calibrated with historical release data to generate anticipated release curves for new formulation batches [p.15341/col.1/para/2] (setting the constructed simulation model with the modified parameters used in performing the simulation to generate the given simulation-based release profile, as the model predicting the release profile of the substance from the material matrix). Yeom teaches calibrating a model by iteratively changing the input parameters until the simulation results converge on the experimental results [p.7/para.1] (until a given simulation-based release profile matches release system characteristic data, iteratively: (i) modifying parameters … and (ii) performing a simulation).
Response to Arguments
Applicant’s arguments, see pp.12-15, filed 27 February 2026, with respect to the rejections of claims 1-20 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of newly found prior art reference, Yeom. Yeom teaches a known calibration technique in pharmaceutical simulation modeling of iteratively modifying input parameters, running a simulation, comparing the simulation outputs to experimental data, and repeating the process until the simulation results are in agreement with the experimental results, which, when combined with the teachings of Irizarry and Irizarry PG-PUB, renders the claimed invention obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily A Darrigrand whose telephone number is (571)272-1098. The examiner can normally be reached Monday-Thursday 7:00AM-4:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry Riggs, can be reached at (571) 270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.A.D./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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