DETAILED ACTION
The amendment filed on 07/20/2026 has been entered and fully considered. Claims 1-13 are pending, of which claim 1-3 and 10-13 are amended.
Response to Amendment
In response to amendment, the examiner maintains rejection under 35 U.S.C. 101, and modifies rejection over the prior art established in the previous Office 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 Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-13 are rejected under 35 U.S.C. § 101 because the claimed inventions are directed to judicial exceptions without integrating the exceptions into practical applications and without reciting additional elements that amount to significantly more than the exceptions.
Step 1: Statutory category
Claims 1-9 recite devices or systems, and claims 10-13 recite methods. Accordingly, the claims fall within statutory categories identified in 35 U.S.C. § 101.
Step 2A, Prong One: Judicial exception
Independent claims 1-3 and 10-12 recite mathematical concepts. In particular, the claims recite:
calculating quantitative measurement information based on measurement data;
modeling quantitative estimation information using a reaction model;
providing the quantitative measurement information to the reaction model to estimate a parameter of the reaction model;
calculating, based on the estimated parameter, quantitative estimation information at an arbitrary point in time or a period until the information reaches a predetermined threshold; and
modifying the reaction model by including an integrated error term, setting an additional reaction in accordance with an initial value, or setting a time difference relating to the start of an analysis.
These limitations recite mathematical relationships and mathematical calculations. The reaction model defines mathematical relationships among measurement information, temperature, humidity, time, and one or more estimated parameters. Estimating the parameter by providing quantitative information to the reaction model and using that parameter to calculate a value at a selected time or a time to reach a threshold constitute mathematical calculations performed using the model.
In claims 1 and 10, the limitation that “an integrated error term for integration of errors based on setting values of the temperature and the humidity” is included in the reaction model further defines the mathematical structure of the reaction model and the manner in which errors associated with temperature and humidity variables are mathematically represented and integrated.
In claims 2 and 11, setting an additional reaction in the reaction model in accordance with an initial value defines an additional mathematical relationship within the model based on an initial-condition variable.
In claims 3 and 12, setting a time difference relating to the start of an analysis in the reaction model defines a temporal offset within the mathematical model.
Claims 4, 7, and 8 expressly add further mathematical concepts. Claim 4 applies an Arrhenius or modified Arrhenius equation to the reaction model. Claims 7 and 8 calculate statistical outputs, including confidence intervals and quantiles. Thus, these claims additionally recite mathematical formulas and statistical calculations.
Accordingly, claims 1-13 recite abstract ideas in the form of mathematical concepts under MPEP § 2106.04(a)(2).
Step 2A, Prong Two: No integration into a practical application
The claims do not integrate the recited mathematical concepts into a practical application.
Claims 1-3 recite a memory that stores a reaction model and program and a processor that executes the program to implement the claimed acquisition, calculation, estimation, and prediction functions. Claims 10-12 similarly state that the methods are executed by a processor coupled to a memory. The processor and memory are recited generically and perform their ordinary functions of storing information and executing instructions. The claims do not recite a particular processor architecture, memory arrangement, data structure, sampling algorithm, or improvement in the manner in which a computer stores, retrieves, or processes information.
Although the claims acquire measurement data “obtained by an analysis of a sample using an analysis device,” the claims do not require the processor to operate, control, calibrate, or otherwise improve the analysis device. The claims instead receive preexisting measurement data generated by the separate analysis device and use that data as input to the mathematical model. Mere data gathering that supplies values for use in a mathematical calculation is insignificant extra-solution activity and does not integrate the exception into a practical application.
The calculated outputs likewise do not control a subsequent physical process or cause a treatment, storage adjustment, manufacturing operation, or other action to be performed. The claims terminate with calculating quantitative estimation information or calculating a period until a threshold is reached. Thus, the results of the mathematical analysis are merely generated as information rather than being applied to effect a particular treatment or transformation.
Applicant's specification may describe particular embodiments employing multidimensional chromatographic data, Markov chain Monte Carlo, Hamiltonian Monte Carlo, a No-U-Turn Sampler, or a particular likelihood function. However, the claims do not recite those particular techniques. The claims broadly encompass estimating a parameter using any reaction model and any processing technique meeting the claimed functional result. Eligibility is evaluated based on the limitations appearing in the claims, and unclaimed implementation details from the specification cannot supply an integration limitation absent from the claims.
The integrated error term of claims 1 and 10, the additional-reaction feature of claims 2 and 11, and the time-difference feature of claims 3 and 12 alter the mathematical structure or inputs of the reaction model. Even assuming these features improve the accuracy, convergence, or reliability of the resulting estimation, the claims do not recite a particular improvement to processor operation or to the analysis device. Rather, the alleged improvement is an improvement in the mathematical model and the information produced by that model. An improvement in the accuracy of a mathematical prediction, without a claimed application of that prediction to improve or control another technology, does not by itself integrate the mathematical concept into a practical application.
The remaining limitations do not alter this conclusion:
Claim 4 specifies the mathematical equation used in the model.
Claim 5 adds light as another input or acceleration factor.
Claim 6 stores multiple mathematical reaction models in memory.
Claims 7 and 8 specify numerical or statistical forms of the calculated output.
Claims 9 and 13 limit the field of use to pharmaceutical formulations, drug substances, active ingredients, or impurities.
These limitations refine the inputs, equations, stored models, outputs, or field of use of the mathematical analysis, but do not apply the analysis in a manner that improves or controls a physical pharmaceutical-analysis process.
Therefore, claims 1-13 do not integrate the recited judicial exceptions into practical applications.
Step 2B: No significantly more
The claims do not recite additional elements, individually or as an ordered combination, that amount to significantly more than the mathematical concepts.
The additional computer elements are the memory, program, and processor. The memory stores the reaction model and program, and the processor executes the program to carry out the claimed mathematical calculations. These are generic computer functions that merely provide the technological environment in which the mathematical model is implemented. The claims do not recite any nonconventional processor configuration or specific computer implementation that changes how the computer itself operates.
The acquisition of measurement data also does not supply an inventive concept. The data are expressly described as already having been “obtained by an analysis of a sample using an analysis device.” Accordingly, the claimed acquirer merely obtains existing data for use by the model. The processor thereafter calculates quantitative information, fits or estimates the reaction-model parameter, and calculates a predicted value or threshold time. Considered as an ordered combination, the claims therefore amount to obtaining data, applying a mathematical model to the data, and outputting the mathematical result using generic computer components.
The claimed integrated error term, additional reaction, and time difference are part of the judicial exception itself because they define the mathematical relationships and calculations performed by the reaction model. Those mathematical features cannot supply “significantly more” merely by being implemented on the recited generic processor and memory. Likewise, the dependent-claim features specifying an Arrhenius equation, light as an input factor, multiple stored models, confidence intervals, quantiles, or a pharmaceutical field of use do not add a technological implementation beyond the mathematical analysis.
Accordingly, the claims, considered individually and as ordered combinations, do not recite an inventive concept sufficient to transform the claimed mathematical concepts into patent-eligible applications.
Conclusion
Claims 1-13 recite mathematical concepts comprising reaction-model parameter estimation and predictive calculations. The remaining limitations merely provide data to the mathematical model, implement the model using generic processor and memory components, specify additional mathematical variables or outputs, or limit the analysis to pharmaceutical subject matter. Therefore, claims 1-13 are directed to judicial exceptions without integration into practical applications and without significantly more, and are ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-4 and 7-13 are rejected under 35 U.S.C. § 103 as being unpatentable over Waterman et al. (Pharmaceutical Research, 2007) (Waterman).
Regarding claim 1, Waterman teaches “a sample analysis device comprising.”
“a memory storing at least one reaction model and a program; and a processor coupled to the memory and configured, by execution of the program, to implement”
Waterman teaches:
“The propagation of errors for the extrapolated shelf-life estimations was carried out using a custom software program developed on the SAS statistical software package.” (page 782, par 7).
Waterman further teaches:
“The Monte-Carlo computer program uses the Normal distribution to generate 5,000 simulated experiments, and applies Eq. 5 to the data from each simulated experiment, in order to construct confidence limits on degradation rates at any temperature-RH condition.” (page 788, par 1).
Waterman therefore expressly teaches a computer software program that applies a stored reaction equation and performs Monte Carlo simulation and multiple regression. Although Waterman does not separately label the executing hardware as a “processor” and “memory,” it would have been obvious to execute Waterman’s custom SAS software program using a processor coupled to memory storing the software program and Equation 5. Such an implementation is the predictable computer arrangement for storing and executing Waterman’s disclosed program and permits the disclosed 5,000 simulations, regression fitting, and shelf-life extrapolation to be performed automatically. Waterman expressly states that its protocol and analyses provide “accurate and precise shelf-life estimations in a reduced time from current state of the art.” (abstract).
“an acquirer that acquires a plurality of measurement data pieces obtained by an analysis of a sample using an analysis device under a plurality of analysis conditions, the analysis conditions including a temperature and a humidity as acceleration factors”
Waterman teaches:
“Tablets were studied using bottles containing salt slurries (specifically sodium chloride or magnesium chloride) or desiccants (1 g Sorb-It canisters) to control the relative humidity. After storage in 20, 30, 40, 50 and 70°C chambers for 3 and 6 weeks, tablets were removed and stored at 5°C.” (page 781, par 2).
Waterman also teaches that the samples were analyzed using an HPLC system or color analyzer. For example:
“The chromatographic system consisted of an Agilent 1100 HPLC chromatograph … equipped with a binary solvent delivery system, thermostated column compartment, an autosampler and a photodiode array detector.” (page 782, par 2).
The HPLC chromatograph or color analyzer constitutes the claimed analysis device. The analytical measurements taken from samples stored at multiple combinations of temperature, relative humidity, and time constitute the plurality of measurement data pieces obtained under the plurality of analysis conditions.
“a quantitative information calculator that calculates, based on the plurality of measurement data pieces, a plurality of quantitative measurement information pieces of a substance included in the sample”
Waterman teaches:
“Quantification was achieved by comparing chromatographic peak areas from a salicylic acid … reference material as a standard.” (page 782, par 2).
Waterman further teaches:
“A calibration curve of ΔE* values versus the concentration of dehydroascorbic acid … was used to quantify the amount of degradant in the ascorbic acid tablets.” (page 782, par 3).
Waterman’s chromatographic peak-area comparison and calibration curve convert the detector measurements into quantitative concentrations or amounts of degradation products contained in the sample. Each calculated concentration at a respective time, temperature, and humidity condition is a quantitative measurement information piece of a substance included in the sample.
“an estimator that retrieves the reaction model stored in the memory, models quantitative estimation information of the substance with use of the reaction model, and provides the plurality of quantitative measurement information pieces calculated by the quantitative information calculator to the reaction model to estimate a parameter of the reaction model”
Waterman teaches a humidity-corrected Arrhenius reaction model:
“The effect of relative humidity on reaction rates for solid pharmaceuticals can be expressed by the following equation:
ln k = ln A − Ea/(RT) + B(%RH),
where k is the rate constant, A, Ea and B are fit constants with the variables temperature, T, and percent relative humidity (%RH).” (page 785, par 2).
Waterman further teaches:
“These data were fit with a multiple regression package on SAS to determine three parameters, with each set of data.” (page 783, par 0).
Waterman’s SAS program applies the stored humidity-corrected Arrhenius equation to the quantitative degradation information measured at the different conditions and uses multiple regression to estimate the three model parameters (A), (E_a), and (B). Thus, the stored equation is retrieved and applied by the software, the quantitative measurement information is supplied to the equation, and the parameters of the reaction model are estimated.
“a calculator that calculates, based on the parameter estimated by the estimator, quantitative estimation information of the substance at an arbitrary point in time or calculates information in regard to a period of time until quantitative estimation information of the substance reaches a predetermined threshold value”
Waterman teaches:
“The shelf-life at a given condition is set by the time it takes to form a specific degradation product or the total of all degradation products.” (page 780, par 2).
Waterman further teaches that the regression fits:
“were extrapolated to the shelf-life conditions.” (page 783, par 0).
Waterman’s Table IV reports, for example, a “Predicted Time for 0.2% Increase in Major Degradant Peak.” The predicted period until a degradation product reaches the specified 0.2% level constitutes information regarding the period until the quantitative estimation information reaches a predetermined threshold. The prediction is calculated using the fitted temperature- and humidity-dependent reaction-model parameters.
“wherein an integrated error term for integration of errors based on setting values of the temperature and the humidity set under the plurality of analysis conditions is included in the reaction model”
Waterman teaches:
“In this program, an error in the concentration of degradation product at a given time was assigned a value based on either multiple sample measurements at that condition or based on overall quantification of error for the entire data set.” (page 782, par 7; page 783, par 0).
Waterman further teaches:
“The error was propagated through the logarithm function for each temperature and relative humidity condition. These data were fit with a multiple regression package on SAS to determine three parameters, with each set of data. The fits were extrapolated to the shelf-life conditions with the overall error determined based on the distribution of the points generated.” (page 783, par 0).
Waterman additionally states:
“The error associated with each degradant at a given time, temperature and relative humidity contributes to the eventual error in the extrapolated predicted stability.” (page 788, par 2).
Thus, Waterman assigns error values to quantitative measurements obtained at the respective temperature and humidity conditions, propagates those errors through the logarithmic humidity-corrected Arrhenius model, integrates the resulting condition-specific errors in a multiple-regression fit, and determines an overall prediction error from the combined distribution. Waterman therefore teaches an integrated error term for integrating errors associated with the temperature and humidity settings used under the plurality of analysis conditions.
Regarding claim 2, Waterman teaches the memory, processor, acquirer, quantitative information calculator, estimator, and calculator for the reasons set forth regarding claim 1.
“wherein an additional reaction is set in the reaction model in accordance with an initial value”
Waterman teaches:
“Assuming either that a particular solid-state reaction proceeds from each form directly or through a reactive-form intermediate, there will be an initial rapid rate of reaction due to any reactive-form material present … followed by a slower rate (steady state) as this material is consumed as represented kinetically below.” (page 783, par 2).
Waterman represents the initial reaction as:
“d[P]/dt = k₂[Dreactive] initial” (page 783, Eq. 3).
and separately represents the subsequent steady-state reaction based on crystalline drug as Equation 4. Waterman explains:
“the first part of the product formation curve is dominated by the small amount of rapidly reacting material, while the latter part of the reaction is dominated by the slower, but more prevalent crystalline phase.” (page 783, par 3).
Waterman therefore sets an initial reaction in the kinetic model according to the initial amount of reactive-form material, in addition to the slower steady-state reaction. The initial concentration determines the contribution of the additional initial reaction to modeled product formation and therefore satisfies the broadly recited additional reaction set in accordance with an initial value.
Regarding claim 3, Waterman teaches the memory, processor, acquirer, quantitative information calculator, estimator, and calculator for the reasons set forth regarding claim 1.
“wherein a time difference in regard to start of an analysis is set in the reaction model”
Waterman teaches:
“In a number of solid pharmaceutical formulations, the presence of an inhibitor (e.g., antioxidant) prevents a degradation pathway until the inhibitor is substantially consumed, at which time the drug degradation can proceed rapidly. The reaction therefore has a lag time before proceeding.” (page 784, par 5).
Waterman’s Figure 7 is expressly a calculated reaction model and states:
“Model assumes k = 0.023 for the inhibitor, with 90% of the inhibitor needed to be decomposed before reaction to give product P begins.” (Fig. 7).
The stability analysis begins at the initial storage time, while the modeled product-forming reaction begins only after the inhibitor has reached the specified degree of decomposition. Waterman thereby sets in the reaction model a lag or time difference between the beginning of the stability analysis and the beginning of the modeled degradation reaction. Waterman further uses that modeled lag in the Arrhenius shelf-life extrapolation shown in Figure 8.
Regarding claim 4, Waterman teaches all limitations of claim 1 as set forth above.
“wherein an Arrhenius equation or a modified Arrhenius equation is applied to the reaction model”
Waterman teaches:
“The effect of relative humidity on reaction rates for solid pharmaceuticals can be expressed by the following equation: ln k = ln A − Ea/(RT) + B(%RH).” (page 785, par 2).
Waterman expressly characterizes Equation 5 as a “moisture-corrected Arrhenius equation.” The equation is a modified Arrhenius equation that includes a relative-humidity term and is applied to model the pharmaceutical degradation rate.
Regarding claim 7, Waterman teaches all limitations of claim 1 as set forth above.
“wherein quantitative estimation information of the substance includes a quantitative value, a confidence interval or a quantile of the substance at an arbitrary point in time”
Waterman teaches:
“The Monte-Carlo computer program uses the Normal distribution to generate 5,000 simulated experiments … in order to construct confidence limits on degradation rates at any temperature-RH condition.” (page 788, par 1).
Waterman’s predicted degradation rate is a quantitative value representing formation of the degradation product over time, and the Monte Carlo distribution supplies confidence limits for that quantitative prediction. Waterman therefore teaches quantitative estimation information including a quantitative value and confidence interval.
Regarding claim 8, Waterman teaches all limitations of claim 1 as set forth above.
“wherein information in regard to the period of time includes a value, a confidence interval or a quantile in a period of time until quantitative estimation information of the substance reaches a predetermined threshold value”
Waterman’s Table IV expressly reports a:
“Predicted Time for 0.2% Increase in Major Degradant Peak (90% Confidence).”
For example, Waterman reports predicted periods such as “245 ± 151 days” and “1.5 ± 0.8 years.” These are predicted time values with confidence information for the period until the degradation product reaches the predetermined 0.2% threshold.
Regarding claim 9, Waterman teaches all limitations of claim 1 as set forth above.
“wherein the sample includes a formulation or a drug substance, and the substance includes an active ingredient or an impurity present in the formulation or the drug substance”
Waterman states that the disclosed protocol determines:
“the chemical stability of both solid drug substances and formulated solid dosage forms.” (page 780, par 2).
Waterman also teaches separately determining:
“the rate of formation of each degradation product.” (page 780, par 2).
Waterman’s drug substances and formulated tablets correspond to the claimed drug substance and formulation. The drug is the active ingredient, while salicylic acid, dehydroascorbic acid, and the other quantified degradation products are impurities present in or formed from the formulation or drug substance.
Regarding claim 10, Waterman teaches a sample analysis method executed by a processor coupled to a memory for the same reasons set forth regarding the processor, memory, and program of claim 1.
Waterman further teaches acquiring measurement data from HPLC or colorimetric analysis of samples stored under multiple temperature and humidity conditions; calculating quantitative degradant information from chromatographic peak areas or calibration curves; applying the humidity-corrected Arrhenius model to the quantitative information using multiple regression to estimate (A), (Ea), and (B); and calculating a predicted shelf-life or time to a specified degradant threshold.
Waterman also teaches the recited integrated error term because:
“The error was propagated through the logarithm function for each temperature and relative humidity condition,” (page 783, par 0).
the resulting data were fit using multiple regression, and:
“the overall error [was] determined based on the distribution of the points generated.” (page 783, par 0).
Accordingly, Waterman teaches the method limitations of claim 10 for the same reasons set forth regarding claim 1.
Regarding claim 11, Waterman teaches the sample analysis method limitations for the reasons set forth regarding claim 10.
Waterman further teaches setting an additional reaction in accordance with an initial value because Waterman models an initial reaction according to:
“d[P]/dt = k₂[Dreactive]initial”
followed by a separate steady-state reaction associated with crystalline material.
Accordingly, Waterman teaches the additional limitation of claim 11 for the reasons set forth regarding claim 2.
Regarding claim 12, Waterman teaches the sample analysis method limitations for the reasons set forth regarding claim 10.
Waterman further teaches setting a time difference relating to the start of the analysis because its calculated model requires 90% decomposition of an inhibitor before the modeled product-forming reaction begins and expressly states that:
“The reaction therefore has a lag time before proceeding.”
Accordingly, Waterman teaches the additional limitation of claim 12 for the reasons set forth regarding claim 3.
Regarding claim 13, Waterman teaches the sample analysis method of claim 10 as set forth above.
Waterman further applies the method to:
“solid drug substances and formulated solid dosage forms” (page 780, par 2).
and calculates the formation of individual degradation products. The analyzed sample therefore includes a formulation or drug substance, and the quantified substance includes the active drug or an impurity/degradation product present in the formulation or drug substance.
Claims 5-6 are rejected under 35 U.S.C. § 103 as being unpatentable over Waterman in view of Gonzalez-Gonzalez et al. (Phamaceutics, 2022) (Gonzalez).
Regarding claim 5, Waterman teaches all limitations of claim 1 as set forth above. Waterman does not expressly include light as another acceleration factor.
However, Gonzalez teaches:
“Stability studies were designed for monitoring and evaluating the quality of Active Pharmaceutical Ingredients (API) and Finished Pharmaceutical Products (FPP) under the influence of different factors such as environmental conditions (temperature, moisture, light).” (page 1, par 1).
Gonzalez further teaches that factors influencing APS data analysis include:
“external conditions (temperature, humidity, light, and oxygen concentration).” (page 7, par 4).
Waterman and Gonzalez are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to accelerated pharmaceutical-stability analysis using environmental stress conditions and reaction models to predict degradation and shelf life.
Therefore, it would have been obvious to modify Waterman’s accelerated pharmaceutical-stability analysis system to include light as an additional acceleration factor, as taught by Gonzalez, so that the analysis accounts for photolytic degradation in addition to temperature- and humidity-dependent degradation. One of ordinary skill in the art would have been motivated to improve the ability of the pharmaceutical-stability analysis system to account for environmental factors that influence degradation, as suggested by Gonzalez’s teaching that stability studies evaluate the influence of “temperature, moisture, [and] light.”
Regarding claim 6, Waterman teaches all limitations of claim 1 as set forth above, including a memory storing a reaction model and a software program applying that model. Waterman does not expressly state that a plurality of alternative reaction models are stored in the memory.
Gonzalez teaches:
“Experimental results are modelled using a range of different equations (e.g., zero, first, second, diffusion, and Avrami kinetics) to understand which kinetic model governs the degradation process.” (page 7, par 2).
Gonzalez therefore teaches making a plurality of different reaction models available for evaluating experimental degradation data.
Waterman and Gonzalez are analogous art because they both relate to accelerated pharmaceutical-stability analysis that selects and applies kinetic reaction models to measured degradation data.
Therefore, it would have been obvious to store Gonzalez’s plurality of zero-order, first-order, second-order, diffusion, and Avrami reaction models in the memory used by Waterman’s computer program so that the program can apply and compare the available models to determine the model governing a particular degradation process. One of ordinary skill in the art would have been motivated to improve the ability of the pharmaceutical-stability analysis system to model different degradation mechanisms, as suggested by Gonzalez’s teaching that the different equations are used “to understand which kinetic model governs the degradation process.”
Response to Arguments
Applicant's arguments filed 07/20/2026 have been fully considered but they are not persuasive.
Response to Arguments – 35 U.S.C. §101
Applicant argues that the claims are not directed to a mental process because the specification describes an integro-differential model, Markov chain Monte Carlo sampling, Hamiltonian Monte Carlo, and multidimensional detector data, and asserts that these operations cannot practically be performed in the human mind. Applicant further argues that the claims integrate any alleged judicial exception into a practical application by improving parameter estimation and that the recited features provide an inventive concept.
The arguments are not persuasive. Eligibility is determined based on the language of the claims rather than unclaimed details disclosed in the specification. Although the specification describes particular Bayesian estimation techniques and computational implementations, the independent claims do not recite Markov chain Monte Carlo, Hamiltonian Monte Carlo, multidimensional detector processing, or any other specific algorithm for performing the claimed estimation. Instead, the claims broadly recite acquiring measurement data, calculating quantitative information, estimating parameters of a reaction model, and calculating predicted values or threshold times. These steps amount to collecting data, evaluating the data using mathematical relationships, and producing predictive results, which constitute mathematical concepts and mental processes under the 2019 Revised Patent Subject Matter Eligibility Guidance. Reciting that the method is performed by a generic processor coupled to a memory does not integrate the judicial exception into a practical application because the processor merely performs the abstract calculations as a tool without improving the functioning of the computer itself or any other technology.
Applicant also argues that the claimed integrated error term, additional reaction, and time-difference features constitute specific technological improvements and that the Office’s obviousness rationale confirms such an improvement. The argument is not persuasive. The rejection under 35 U.S.C. §103 does not concede that these features improve the functioning of a computer, analysis device, or other technology. Rather, Waterman demonstrates that incorporating experimental errors into a temperature- and humidity-dependent regression model, modeling an initial reaction according to an initial quantity of reactive material, and incorporating a lag time into a degradation model were known techniques for pharmaceutical shelf-life estimation. The benefits produced by these features concern the accuracy and reliability of the resulting mathematical prediction, not an improvement in the operation of the processor, memory, or sample-analysis device. Accordingly, the asserted benefits do not establish integration of the recited mathematical concepts into a practical application.
Accordingly, the rejection under 35 U.S.C. §101 is maintained.
Response to Arguments—35 U.S.C. §103
Applicant’s arguments have been fully considered but are not persuasive. The rejection has been revised as set forth above to rely primarily on Waterman’s disclosed accelerated shelf-life analysis system, including its reaction models, statistical software, error propagation, initial-value-dependent reaction equations, and modeled lag time. Accordingly, Applicant’s arguments directed to the prior characterization of Gonzalez as the primary reference do not address the rejection as presently formulated.
Claims 1 and 10
Applicant argues that the claimed error must specifically represent an unobserved difference between a chamber’s nominal temperature and humidity settings and the actual conditions within the chamber, and that Waterman merely propagates measurement imprecision. The argument is not commensurate with the scope of the claims. Claims 1 and 10 recite errors “based on setting values” of temperature and humidity, but do not require calculating a difference between nominal and actual conditions, do not require that the actual conditions be unmeasured, and do not specify a particular likelihood function or location of the error term within an equation.
Waterman expressly assigns an error to degradation measurements obtained at each experimental condition, propagates that error “through the logarithm function for each temperature and relative humidity condition,” fits the resulting data using multiple regression, and determines an “overall error” from the combined distribution. Thus, the errors associated with measurements obtained under the respective temperature and humidity settings are incorporated into the same computational reaction-model fitting process used to estimate the model parameters and extrapolate shelf life. Applicant’s narrower embodiments involving actual-versus-setpoint deviations, Bayesian likelihood surfaces, or a particular integrated-error formula are described in the specification but are not recited in claims 1 and 10. Accordingly, Waterman teaches the claimed integrated error term under the broadest reasonable interpretation of the claim language.
Claims 2 and 11
Applicant argues that Waterman merely observes an initial rapid reaction followed by a slower reaction and does not affirmatively set an additional reaction in a model according to an initial value. The revised rejection, however, does not rely solely on Waterman’s observation of biphasic behavior. Waterman expressly represents the initial reaction kinetically as:
“d[P]/dt = k₂[Dreactive]initial”
and separately represents the subsequent steady-state reaction associated with crystalline material. Waterman further explains that “the first part of the product formation curve is dominated by the small amount of rapidly reacting material,” whereas the later part is dominated by the slower crystalline phase. Waterman therefore affirmatively includes an additional initial reaction in its kinetic model, with the contribution of that reaction determined by the initial amount of reactive-form material, Claims 2 and 11 do not require Applicant’s particular function (f(T,H)), addition to (β0), or the specific formulas discussed in the specification. Accordingly, Waterman teaches an additional reaction set in the reaction model in accordance with an initial value.
Claims 3 and 12
Applicant argues that Waterman does not teach setting a time difference in the reaction model and that merely identifying differing reaction rates does not satisfy the limitation. The revised rejection instead relies on Waterman’s inhibitor model. Waterman expressly teaches that an inhibitor prevents a degradation pathway until the inhibitor is substantially consumed, after which degradation proceeds rapidly, and states that “[t]he reaction therefore has a lag time before proceeding.”
Waterman’s calculated model further assumes that 90% of the inhibitor must decompose before formation of the degradation product begins. Thus, the analysis begins at the initial storage time, the product-forming reaction remains inactive while the inhibitor is present, and the model transitions to the active degradation state only after the specified inhibitor-decomposition condition is reached. This modeled lag constitutes a time difference between the start of the analysis and the start of the modeled reaction. Claims 3 and 12 do not require shifting an integral’s lower limit from zero to (-\Delta t) or any other particular mathematical implementation described in the specification. Accordingly, Waterman teaches the broadly recited time difference set in the reaction model.
Claims 4-9 and 13
Applicant does not present separate substantive arguments for claims 4–9 and 13, but instead asserts that those claims are patentable because they depend from claims 1 or 10. Because the arguments regarding claims 1 and 10 are not persuasive, the dependency argument likewise does not overcome the rejections. Waterman additionally teaches the modified Arrhenius equation, confidence information, predicted time to a degradation threshold, and analysis of drug substances, formulations, active ingredients, and degradation products, as set forth in the rejection. Gonzalez is additionally relied upon for light as an acceleration factor in claim 5 and for storing or making available a plurality of kinetic reaction models in claim 6.
Accordingly, Applicant’s arguments do not overcome the rejections of claim 1-13 under 35 U.S.C. § 103, and the rejections are maintained as revised.
Conclusion
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 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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/XIAOYUN R XU, Ph.D./ Primary Examiner, Art Unit 1797
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