DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Apr 21 2026 has been entered.
Applicant’s response, filed Apr 21 2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
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 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.
Claim Status
Claims 1-2, 7-8, 10-11, 13, 15-16, 19, 21, 25, 30, 32-34, 36-37, 42-43, 45, 51, 56, 65, 67, and 69 are pending.
Claims 3-6, 9, 12, 14, 17-18, 20, 22-24, 26-29, 31, 35, 38-41, 44, 46-50, 52-55, 57-64, 66, 68, and 70 are canceled.
Claims 1-2, 7-8, 10-11, 13, 15-16, 19, 21, 25, 30, 32-34, 36-37, 42-43, 45, 51, 56, 65, 67, and 69 are rejected.
Priority
The instant Application claims domestic benefit to US provisional application 62/826,910, filed Mar 29 2019.
Applicant's claim for the benefit of a prior-filed application, PCT/US2020/025444, filed Mar 27 2020, is acknowledged.
Accordingly, each of claims 1-2, 7-8, 10-11, 13, 15-16, 19, 21, 25, 30, 32-34, 36-37, 42-43, 45, 51, 56, 65, 67, and 69 are afforded the effective filing date of Mar 29 2019.
Claim Objections
The claims are objected to because of the following informalities. The instant objection is newly stated and is necessitated by claim amendment.
Claim 36 recites, in the preamble, “a controller of real-world bioreactor”, which should be amended to recite “a controller of a real-world bioreactor”.
Claim Rejections - 35 USC § 101
The outstanding rejections under 35 USC 101 are hereby withdrawn in view of further consideration of the instant claims as currently amended. The instant claims include steps that are in addition to the recited judicial exceptions that provide for integration of the recited exceptions into a practical application of those exceptions. Specifically, the steps directed to “controlling, by a controller, the real-world bioreactor in accordance with the control setting” in method claim 1 and “control, by the controller, the real-world bioreactor in accordance with the control setting” in non-transitory computer-readable medium claim 36, where the computer-readable medium is operably coupled to a controller of real-world bioreactor, are additional elements that integrate the judicial exceptions present in the claims into a practical application at Step 2A, Prong 2, as supported by Applicant’s remarks as submitted Apr 21 2026 at p. 12-13.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 7-8, 10-11, 13, 15-16, 19, 21, 25, 30, 32-34, 36-37, 42-43, 45, 51, 56, 65, 67, and 69 are rejected under 35 U.S.C. 103 as being unpatentable over Famili et al. (US 2016/0364,520; cited on the Nov 14 2022 IDS) in view of Lapin et al. (Chemical Engineering Science, 2006, 43:4783-4797; cited on the Sep 29 2021 IDS). The instant rejection is newly stated and is necessitated by claim amendment.
The prior art to Famili discloses models and methods useful for optimizing cell lines (abstract). Famili, indicated by the open circles, teaches the instant features, indicated by the closed circles, as follows. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
Claim 1 discloses a method of computationally modeling a bioreactor to account for spatial inhomogeneity. Claim 36 discloses a non-transitory computer-readable medium storing instructions for computationally modeling a bioreactor to account for spatial inhomogeneity, wherein the computer-readable medium is operably coupled to a controller of real-world bioreactor, wherein the instructions, when executed by one or more processors, cause the one or more processors to perform a method.
Famili teaches methods and computer readable medium or media comprising commands or instructions for a processor to carry out models and methods useful for optimizing cell lines by modelling improved characteristics of the cell lines [0007; 0167-0168]. See below for teachings by Lapin regarding spatial inhomogeneity.
The method of claims 1 and 36 comprise:
receiving a plurality of current values of process variables, the process variables describing virtual contents of a plurality of portions of the modeled bioreactor, and the virtual contents including virtual cellular biomass in a virtual extracellular solution;
Famili teaches that the models are based on a data structure relating a plurality of reactants to a plurality of reactions wherein each of the reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product (i.e., process variables) [0050]. Famili teaches that the data structure can be stored on a computer readable medium or media and accessed (i.e., received) to provide the data structure for use with a method of the invention [0080]. Famili teaches that the term “reaction” means a conversion that consumes a substrate or forms a product that occurs in or by a cell, where the “reactant” is a chemical that is a substrate or a product of a reaction that occurs in or by a cell, which, in an in silico model or data structure, is a representation of a chemical that is a substrate or a product of a reaction that occurs in or by a cell (i.e., virtual contents of the modeled bioreactor) [0051-0055]. As Famili teaches examining or predicting biomass production (i.e., virtual cellular biomass) [0085; 0120; 0173-0174; 0180] in culture media where a nutrient is provided in the extracellular environment [0047; 0049; 0079; 0082; 0084; 0213-0242], it is considered that the model must include information about virtual cellular biomass in a virtual extracellular solution as instantly claimed.
Famili does not teach a plurality of portions of the modeled bioreactor. See below for teachings by Lapin.
computing, by processing hardware of a computing system, new values of the process variables during a simulated time period, at least in part by generating a plurality of constraints on flux rates of metabolic fluxes describing the virtual cellular biomass by modeling one or more effects of at least some of the current values of the process variables on metabolic reaction kinetics, computing the flux rates of the metabolic fluxes by performing flux balance analysis subject to a metabolic objective and the generated plurality of the constraints on the flux rates, computing rates of change of at least some of the process variables based at least in part on the computed flux rates, and updating one or more of the current values of the process variables at least in part by integrating one or more of the computed rates of change for a virtual time step within the simulated time period;
Famili teaches performing the method on a computer [0080] to produce an in silico model (i.e. simulations) of a cell culture [0083]. Famili teaches that the models are based on a data structure relating a plurality of reactants to a plurality of reactions (i.e. metabolic fluxes), wherein each of the reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product (i.e., process variables) [0050]. Famili teaches that information included in the data structure can include a constraint placed on a reaction, where a constraint is an upper or lower boundary for a reaction [0058]. Famili teaches providing an objective function, wherein the objective function is uptake rate of two or more nutrients (i.e., metabolic objective), and determining at least one flux distribution (i.e., rates of metabolic fluxes) that minimizes or maximizes an objective function when the constraint set is applied to the data structure, wherein the at least one flux distribution is predictive of a physiological function of the cell [0074; 0078; 0126]. Famili teaches performing flux balance analysis to determine flux distributions over a period of time [0125-0126]. Famili teaches that the flux balance analysis links the calculated steady state rates from metabolic flux analysis to dynamic changes in metabolite concentrations (i.e., changes in the process variables) in cell culture during the simulation [0010]. As Famili teaches displaying these changes [0087; 0213-0242], it is considered that Famili fairly teaches updating the process variables as instantly claimed. Famili also teaches that the optimal flux distribution can be calculated from a reaction network data structure and a set of constraints as set forth above for all points in this plane by repeatedly solving the linear programming problem while adjusting (i.e., updating) the exchange fluxes defining the two-dimensional space [0181].
computing, by the processing hardware, a metric of the computationally modeled bioreactor based at least in part on the computed new values of the process variables; and
Famili teaches determining predicted total cell and glucose concentrations for cell culture simulations over a time profile [0009; 0126], which reads on a metric as instantly claimed because claim 25 (see below) further limits the metric of the modeled bioreactor to include values of at least one of the process variables at different virtual times within the simulated time period.
generating, by the processing hardware, and based on the metric of the modeled bioreactor a control setting for a real-world bioreactor; and controlling, by a controller, the real-world bioreactor in accordance with the control setting.
Famili teaches that the knowledge obtained by analyzing the data in the context of the reconstructed model is used to identify design parameters that should be monitored or controlled (i.e., control setting) in cell culture to prevent variability in cell culture condition that would result in scale up variability or batch to batch variability [0087]. Famili further teaches that determining the variability under different culture conditions and optimizing or improving the conditions of a cell culture, for example by determining limiting nutrient(s) and providing increased amounts of such nutrients in the media, and that one skilled in the art will readily know appropriate selection of a constraint set or objective function to achieve a desired outcome of a culture condition using the methods and models of the invention (i.e., controlling the real-world bioreactor in accordance with the control setting) [0087]. Famili teaches that while growing cells in a bioreactor, each reactor was controlled via a BioStat DCU (i.e., controller) to control bioreactor conditions [0203].
Famili does not teach modeling a bioreactor to account for spatial inhomogeneity or a plurality of portions of the modeled bioreactor.
However, the prior art to Lapin discloses an Euler–Lagrange modeling approach to characterize the behavior of a heterogeneous cell population in a stirred tank bioreactor with non-ideal mixing that accounts for the heterogeneity present in real reactors in both the biotic and abiotic phases (abstract). Lapin teaches that the simulation model comprises a set of intracellular balance equations which are divided based upon discretization of the bioreactor (p. 4786, col. 1 through p. 4790, col. 1, par. 3), which reads on first and second portions of the bioreactor with first and second current and new process variables as instantly claimed.
Regarding claims 1 and 36, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the method of Famili and Lapin because both references disclose methods for simulation modeling of cells growing in a bioreactor. The motivation to include the Euler-Lagrange approach to characterize the behavior of a heterogeneous cell population in a stirred-tank bioreactor of Lapin would have been to account for the dynamic spatial and temporal variations of the extracellular environment in the bioreactor which the living cells are exposed to, as taught by Lapin (p. 4784, col. 1, par. 1 through col. 2, par. 2).
Regarding claims 2 and 37, Famili in view of Lapin teaches the method of claim 1 and the non-transitory computer-readable medium of claim 36 as described above. Claims 2 and 37 further add that the process variables include at least one of: (i) temperature, (ii) acidity, (iii) one or more variables indicative of total osmolarity of the virtual contents, or (iv) one or more variables indicative of concentration of the virtual cellular biomass and one or more variables indicative of extracellular metabolite concentrations in the virtual extracellular solution, wherein the one or more variables indicative of the extracellular metabolite concentrations in the virtual extracellular solution include one or more variables indicative of concentrations of at least one of: oxygen, carbon dioxide, ammonia, glucose, asparagine, glutamine, glycine, or at least one target metabolic product.
Famili teaches that the models are based on a data structure relating a plurality of reactants to a plurality of reactions wherein each of the reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product (i.e., process variables) [0050]. Famili teaches determining flux through a reaction by accounting for a regulatory event with a modifier, where the modifier can include a change in pH (i.e. acidity), temperature [0158; 0165]. Famili also teaches that osmolarity can affect culture conditions [0257; 0306; 0394]. Famili teaches examining total cell concentrations (i.e. virtual cell biomass) [0009] and metabolite concentrations in cell culture [0010] based on uptake and secretion of the metabolites to determine the concentration of metabolites in the bioreactor media (i.e., virtual extracellular solution) [0126]. Famili teaches examining oxygenation, glucose, glutamine, carbon, nitrogen, sulfur, phosphate, hydrogen, lactate, ammonia, amino acids including asparagine, glutamine, and glycine (i.e. target metabolic products) in the media ([0086; 0180-0181; 0223; 0296; 0366] and Table 1).
Regarding claims 7 and 42, Famili in view of Lapin teaches the method of claims 1-2 and the non-transitory computer-readable medium of claim 36-37 as described above. Claims 7 and 42 further add that the process variables additionally include one or more variables indicative of one or more intracellular metabolite concentrations in the virtual cellular biomass.
Famili teaches examining metabolite concentrations in the cell (i.e., intracellular metabolite concentrations), including glucose [0009].
Regarding claims 8 and 43, Famili in view of Lapin teaches the method of claim 1 and the non-transitory computer-readable medium of claim 36 as described above. Claims 8 and 43 further add that the plurality of the constraints on the flux rates includes an upper limit on at least one of i) glucose uptake, ii) glutamine uptake, iii) asparagine uptake, or iv) oxygen uptake or a lower limit on an energy consumption rate for cellular maintenance.
Famili teaches that a constraint is an upper or lower boundary for a reaction, where a boundary can specify a minimum or maximum flow of mass, electrons or energy through a reaction [0058]. Famili teaches a constraint set for the plurality of reactions examined [0108], including constraining the upper limits of glucose, oxygen, asparagine, and glutamine uptake to experimental levels and constraining the minimum growth rate and biomass production (i.e., an energy consumption rate for cellular maintenance) ([0070; 0215]; Tables 21-23).
Regarding claims 10 and 45, Famili in view of Lapin teaches the method of claim 1 and the non-transitory computer-readable medium of claim 36 as described above. Claims 10 and 45 further add that the one or more effects of at least some of the current values of the process variables on the metabolic reaction kinetics includes 1) an effect of a value of at least one of: i) temperature, ii) acidity, or iii) osmolarity on an upper limit of a metabolite uptake rate; 2) an effect of stress on a lower limit of energy consumption rate for cellular maintenance; or 3) an effect of concentration of at least one of metabolic byproducts.
Famili teaches a constraint set for the plurality of reactions examined [0108], including constraining the upper limits of glucose, oxygen, asparagine, and glutamine uptake to experimental levels ([0070; 0215]; Tables 21-23). Famili teaches determining flux through a reaction by accounting for a regulatory event with a modifier, where the modifier can include a change in pH (i.e. acidity), temperature [0158; 0165]. Famili teaches that a function included in the term can also correlate a boundary value with an environmental condition such as pH or temperature (i.e., 1) i) and ii)) [0059]. Famili teaches models to reduce byproduct formation (i.e., 3)) [0047-0048; 0227-0242; 0272-0274].
Regarding claim 11, Famili in view of Lapin teaches the method of claims 1 and 10 as described above. Claim 11 further adds that modeling the one or more effects of at least some of the current values of the process variables on the metabolic reaction kinetics includes computing the effect of the value of at least one of: i) temperature, ii) acidity, or iii) osmolarity on the upper limit of the metabolite uptake rate by multiplying the upper limit with a correction factor indicative of a reduction in the uptake rate due to non-ideal conditions.
Famili teaches that a function included in the term can also correlate a boundary value with an environmental condition such as pH or temperature [0059] by modifying a change in the presence, absence, or amount of an enzyme that performs a reaction [0158]. Famili teaches that models are used to simulate the phenotypic behavior of an organism under changing genetic circumstances and environmental conditions [0129]. Famili teaches that the regulatory structure includes a general control stating that a reaction is inhibited by a particular environmental condition like pH or temperature [0164-0165]. Famili teaches that a reaction constraint placed on a reaction can be incorporated into the model by multiplying the maximum flux value of a reaction (aj) by a term representing the regulatory reaction [0161; 0188].
Regarding claim 13, Famili in view of Lapin teaches the method of claims 1 and 10 as described above. Claim 13 further adds that modeling the effect of stress includes computing the effect based at least in part on a cumulative effect of a temperature shift on the virtual cellular biomass. Claim 13 is interpreted to further limit option 2) of claim 10. As only one option of claim 10 is required, and Famili teaches at least options 1) and 3) as described above, option 2) and, therefore, the limitations of claim 13 are not required.
Regarding claim 15, Famili in view of Lapin teaches the method of claim 1 as described above. Claim 15 further adds that modeling the one or more effects includes calibrating the modeled effects with experimental data from a real-world cell culture.
Famili teaches performing experiments to verify the models [0020; 0028; 0044-0048] and constraining or choosing the reactions based on experimental data [0063-0064; 0070; 0116; 0128; 0145; 0151; 0173-0174], both of which read on “calibrating” as instantly claimed. Famili also teaches estimating nutrient uptake and byproduct secretion rates will be estimated using bioreactor data, and importing bioreactor data to reconcile the elemental balance of the input and output metabolites, to estimate experimental error [0398].
Regarding claims 16 and 51, Famili in view of Lapin teaches the method of claim 1 and the non-transitory computer-readable medium of claim 36 as described above. Claims 16 and 51 further add that the metabolic objective includes minimizing a ratio of linear combinations of the at least some of the flux rates and squares of flux rates in the flux balance analysis or minimizing a linear combination of at least some of the flux rates in the flux balance analysis.
Famili teaches that the data structure can comprise a set of linear algebraic equations [0090; 0130]. Famili teaches that a time profile of metabolite concentrations is calculated by the transient flux balance analysis in an iterative two-step process, where uptake and secretion rate of metabolites are determined using a metabolic network and linear optimization [0126; 0153; 0172]. Famili teaches that an optimal solution within the set of all solutions can be determined using mathematical optimization methods when provided with a stated objective and a constraint set [0172], by minimizing the linear combination of metabolic fluxes (i.e., minimizing a linear combination of at least some of the flux rates in the flux balance analysis) [0175].
Regarding claim 19, Famili in view of Lapin teaches the method of claim 1 as described above. Claim 19 further adds that the metabolic objective includes maximizing energy consumption rate for cellular maintenance or maximizing a rate of growth of the virtual cellular biomass.
Famili teaches examining objective functions to maximize growth and nutrient uptake (i.e., energy consumption) [0070; 0087; 0093; 0174].
Regarding claims 21 and 56, Famili in view of Lapin teaches the method of claim 1 and the non-transitory computer-readable medium of claim 36 as described above. Claims 16 and 51 further add: 1) receiving one or more variables indicative of flow rates and compositions of one or more virtual input streams into at least one portion of the plurality of portions of the modeled bioreactor, and wherein updating the at least some of the current values of the process variables includes accounting for the flow rates and the compositions of the one or more virtual input streams; 2) receiving one or more variables indicative of properties of a virtual output filter, and computing compositions of one or more virtual output streams from at least one portion of the plurality of portions of the modeled bioreactor using the properties of the virtual output filter, and wherein updating the at least some of the current values of the process variables includes accounting for the compositions of the one or more virtual output streams; or 3) receiving one or more control variables and updating an affected set of the current values of the process variables at least in part based on the received control variables.
Famili teaches adjusting reaction variables based on temperature and pH information [0161; 0164-0165; 0188], which reads on option 3) and a control variable based on the instant specification as published, which discloses that control variables may include temperature settings, acidity settings or any other suitable variables that may represent virtual control settings of the bioreactor [0037].
Regarding claim 25, Famili in view of Lapin teaches the method of claim 1 as described above. Claim 25 further adds that the metric of the modeled bioreactor includes values of at least one of the process variables at different virtual times within the simulated time period.
Famili teaches determining predicted total cell and glucose concentrations for cell culture simulations over a time profile [0009; 0126].
Regarding claim 30, Famili in view of Lapin teaches the method of claim 1 as described above. Claim 30 further adds that receiving the plurality of the current values includes at least one of i) receiving values from a user via a graphical user interface; ii) receiving values based on measurements of a real bioreactor; iii) loading values from computer memory and based on previously computed values; or iv) loading predetermined default values from computer storage.
Regarding claims 32 and 67, Famili in view of Lapin teaches the method of claim 1 and the non-transitory computer-readable medium of claim 36 as described above. Claims 37 and 67 further add that the plurality of portions of the modeled bioreactor include a first portion of the modeled bioreactor and a second portion of the modeled bioreactor; the process variables include first process variables describing first virtual contents of the first portion of the modeled bioreactor; the method further includes receiving a plurality of current values of second process variables, the second process variables describing second virtual contents of a second portion of the modeled bioreactor; and computing the new values of the first process variables during the simulated time period is based in part on the received plurality of the current values of the second process variables.
Famili teaches that at least one reactant in the plurality of reactants or at least one reaction in the plurality of reactions (i.e., virtual contents of the bioreactor) can be annotated with an assignment to a subsystem or compartment, a first substrate or product in the plurality of reactions can be assigned to a first compartment and a second substrate or product in the plurality of reactions can be assigned to a second compartment, and at least a first substrate or product, or more substrates or products, in the plurality of reactions can be assigned to a first compartment and at least a second substrate or product, or more substrates or products, in the plurality of reactions can be assigned to a second compartment [0090]. Famili does not teach a first portion of the modeled bioreactor and a second portion of the modeled bioreactor as instantly claimed.
However, the prior art to Lapin discloses an Euler–Lagrange modeling approach to characterize the behavior of a heterogeneous cell population in a stirred tank bioreactor with non-ideal mixing that accounts for the heterogeneity present in real reactors in both the biotic and abiotic phases (abstract). Lapin teaches that the simulation model comprises a set of intracellular balance equations which are divided based upon discretization of the bioreactor (p. 4786, col. 1 through p. 4790, col. 1, par. 3), which reads on first and second portions of the bioreactor with first and second current and new process variables as instantly claimed.
Regarding claim 33, Famili in view of Lapin teaches the method of claims 1 and 32 as described above. Claim 33 further adds computing the new values of the first process variables during the simulated time period includes computing a gradient of at least one of the first process variables based on a value of a corresponding one of the second process variables, which Famili does not teach.
However, Lapin teaches determining concentration and velocity gradients in bioreactors (p. 4787, col. 1, par. 5 and col. 2, par. 3; p. 4788, col. 2, par. 2; p. 4792, col. 2, par. 3-4).
Regarding claims 34 and 69, Famili in view of Lapin teaches the method of claims 1 and 32 and the non-transitory computer-readable medium of claim 36 as described above. Claims 34 and 69 further add determining one or more velocities associated with the virtual contents of the first portion of the modeled bioreactor, which Famili does not teach.
However, Lapin teaches representing the flow throughout the bioreactor by a velocity field composed of velocity vectors (p. 4785, col. 1, par. 3 through col. 2, par. 1; p. 4787, col. 1, par. 3 through p. 4790, col. 1, par. 3).
Regarding claim 65, Famili in view of Lapin teaches the non-transitory computer-readable medium of claim 36 as described above. Claim 65 further adds that receiving the plurality of the current values includes at least one of: i) receiving values from a user via a graphical user interface; ii) receiving values based on measurements of a real bioreactor; iii) loading values from computer memory and based on previously computed values; or iv) loading predetermined default values from computer storage.
Famili teaches a graphic user interface of the invention can also be capable of sending at least one command for modifying the data structure, the constraint set or the commands for applying the constraint set to the data representation, or a combination thereof (i.e., i)) [0176]. Famili teaches performing experiments to verify the models [0020; 0028; 0044-0048] and constraining or choosing the reactions based on experimental data (i.e., ii)) [0063-0064; 0070; 0116; 0128; 0145; 0151; 0173-0174; 0398]. Famili teaches that reactants to be used in a reaction network data structure of the invention can be obtained from or stored in a compound database (i.e., iii) and iv)) (see at least [0110].
Response to Applicant Arguments
At p. 13-14, Applicant submits that Famili, as acknowledged in the rejection, does not teach modeling a bioreactor to account for spatial inhomogeneity or a plurality of portions of the modeled bioreactor, and that Lapin teaches away from applying their technique for modeling spatial inhomogeneity. Applicant submits that because the claims recite “updating one or more of the current values of the process variables at least in part by integrating one or more of the computed rates of change for a virtual time step within the simulated time period”, one would not attempt to modify any integration-based modeling techniques to implement the spatial modeling approaches described in Lapin.
It is respectfully submitted that this is not persuasive. It is noted that the claims merely recite only that the method is for “computationally modeling a bioreactor to account for spatial inhomogeneity” and that “the process variables describing virtual contents of a plurality of portions of the modeled bioreactor”. The claim recites only that the new values of the process variables are computed by generating a plurality of constraints on flux rates of metabolic fluxes, computing the flux rates of the metabolic fluxes, computing rates of change of at least some of the process variables, and updating one or more of the current values of the process variables at least in part by integrating one or more of the computed rates of change for a virtual time step within the simulated time period. There is nothing in the limitations directing how the method achieves modeling to account for spatial inhomogeneity. The only portion of the claims which address this is in the type of data that is received, which is clearly taught by Lapin as described in the above rejection (p. 4786, col. 1 through p. 4790, col. 1, par. 3).
While Lapin does teach the following limitations as indicated by Applicant:
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Lapin goes on to explain solutions to these issues at the pages cited in the rejection (p. 4786, col. 1 through p. 4790, col. 1, par. 3), including that the simulation model comprises a set of intracellular balance equations which are divided based upon discretization of the bioreactor. Therefore, it is considered obvious that one of ordinary skill in the art would have found it obvious to operate on the discrete portions of the bioreactor data using the methods of Famili to produce updated variables for, theoretically, each of the portions of data. It is noted that this is not the scope of the claims, as described above. That Lapin teaches drawbacks to the state of the art is not considered teaching away from any modification or application of their methods. Applicant has provided no evidence that one of ordinary skill in the art would not reasonably expect to succeed when dividing the bioreactor data into portions and then applying the method of Famili to each of those portions, as discussed in the above rejection.
Conclusion
No claims are allowed.
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 final action.
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/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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