CTNF 18/517,981 CTNF 85241 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. Step 2A, Prong 1: The Claim Recites a Judicial Exception Claim 1 recites, in substance: acquiring measurement data under multiple conditions (temperature and humidity); calculating quantitative information from the data; estimating parameters of a reaction model; and calculating a future value or a time to reach a threshold based on the model. These limitations collectively describe mathematical modeling and data analysis, including: performing calculations, fitting a model to data, and predicting outcomes based on the model. Such subject matter falls within the category of mathematical concepts, which are identified as abstract ideas (see MPEP §2106.04(a)(2)). In particular, estimating model parameters and predicting future values based on a reaction model constitute mathematical relationships, formulas, and calculations. Additionally, these steps can be performed as mental processes, at least in simplified form, by evaluating data and applying mathematical reasoning. Accordingly, claim 1 recites a judicial exception. Claims 2–13 depend from or correspond to Claim 1 and recite similar subject matter, including: acquiring measurement data under multiple analysis conditions (temperature and humidity); calculating quantitative information; estimating parameters of a reaction model; and predicting future values or times to reach thresholds. As discussed with respect to Claim 1, these steps collectively recite mathematical concepts, including: mathematical modeling, parameter estimation, statistical analysis, and predictive calculations. These are abstract ideas (see MPEP §2106.04(a)(2)) and may also be performed as mental processes. Accordingly, Claims 1-13 recite a judicial exception. Step 2A, Prong 2: The Claim Does Not Integrate the Exception into a Practical Application Claim 1 further recites elements such as: “an acquirer,” “a quantitative information calculator,” “an estimator,” and “a calculator.” These elements are recited at a high level of generality and are defined solely by their functions (i.e., acquiring data, calculating values, estimating parameters, and performing calculations). The claim does not specify any particular technological implementation, specialized hardware, or improvement to computer functionality. The additional elements merely: collect data, perform mathematical operations, and output results. The claim does not: improve the functioning of a computer or another technology; effect a transformation of an article to a different state or thing; or apply the abstract idea in any meaningful way beyond generally linking it to a technological environment. The “reaction model including an integrated error term based on temperature and humidity” remains part of the mathematical modeling itself, and does not constitute a practical application beyond the abstract idea. Accordingly, claim 1 does not integrate the judicial exception into a practical application. The additional limitations in Claims 2–13 do not integrate the judicial exception into a practical application. Claim 2 recites: “an additional reaction is set in the reaction model in accordance with an initial value.” This limitation modifies the mathematical model structure only remains within abstract modeling. Claim 3 recites: “a time difference in regard to start of an analysis is set in the reaction model.” This limitation adjusts a model parameter (time offset) is a mathematical manipulation Claim 4 recites: “Arrhenius equation or a modified Arrhenius equation.” This is an explicit mathematical formula directly within abstract ideas. Claim 5 adds: “light is included as the acceleration factor.” This only expands the input variables, and does not change the abstract nature. Claim 6 recites: “a plurality of reaction models.” This involves selecting among multiple mathematical models. Claims 7–8 recite outputs such as: “confidence interval” “quantile” These are statistical calculations, part of mathematical analysis Claims 9 and 13 apply the method/device to pharmaceutical substances This is merely a field-of-use limitation, and does not integrate into a practical application Claims 10–12 (method claims) These claims mirror Claim 1 in method form They recite the same abstract steps: data collection mathematical modeling prediction Conclusion for Step 2A, Prong 2 None of the additional limitations: improve a technological process or device; provide a specific implementation of the abstract idea; or apply the abstract idea in a meaningful, practical way. Instead, they merely: refine the mathematical model, add variables, or specify statistical outputs. Thus, claim 1-13 do not integrate the exception into a practical application. Step 2B: The Claim Does Not Include Significantly More Than the Judicial Exception The additional elements, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. Specifically: acquiring measurement data under different conditions is a routine data collection activity; calculating quantitative information and estimating model parameters are well-understood mathematical operations; predicting future values or threshold times based on a model is a conventional use of mathematical modeling. The recited components (“acquirer,” “calculator,” “estimator”) are generic functional elements that perform their expected functions and do not represent any unconventional arrangement or technological improvement. The inclusion of an “integrated error term” reflects conventional statistical or modeling techniques (e.g., regression, error propagation), which are well-known in the art and do not add an inventive concept. Thus, the claim as a whole merely applies the abstract idea using well-understood, routine, and conventional activities, and does not provide an inventive concept sufficient to transform the nature of the claim into patent-eligible subject matter. The additional elements in Claims 2–13 do not amount to significantly more than the abstract idea. All additional features are: mathematical in nature (e.g., Arrhenius equation, additional reaction, time offset); conventional modeling techniques (e.g., regression, confidence intervals); or routine data processing steps. The claimed components (e.g., acquirer, calculator, estimator): are generic perform well-understood functions do not represent any unconventional arrangement Claim-Specific Observations Adding an Arrhenius equation (Claim 4) reinforces the abstract nature Adding statistical outputs (Claims 7–8) confirms mathematical processing Adding application to pharmaceuticals (Claims 9, 13) is merely a field-of-use limitation Method claims (10–12) do not add any technological improvement In summary, claim 1-13 are directed to a judicial exception (a mathematical concept) and does not include additional elements that integrate the exception into a practical application or amount to significantly more than the exception. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (Pharmaceutics, 2022) (Gonzalez) in view of Waterman et al. (Pharmaceutical Research, 2007) (Waterman) . Regarding claim 1, [1] “An analysis system comprising:” Gonzalez discloses a system for performing accelerated stability analysis of pharmaceutical samples using experimental data obtained under controlled environmental conditions (abstract). Explanation Gonzalez describes a coordinated analytical framework including: data acquisition under controlled conditions modeling of degradation behavior prediction of shelf life Such a framework constitutes an “analysis system” because it includes interacting functional components that process experimental data to generate predictive outputs. [2] “an acquirer configured to acquire measurement data measured under a plurality of analysis conditions including at least temperature and humidity;” Gonzalez teaches: “the stability tests involving direct contact of the drug with temperature and relative humidity conditions inside stability chambers” (step 1, Fig. 11, page 14, par 3). Explanation The stability chambers disclosed in Gonzalez correspond to the claimed acquirer, because they: subject samples to plurality of analysis conditions (different temperature and humidity settings), and produce measurement data (degradation levels, product formation) under those conditions. The measurement data is explicitly obtained at: multiple temperatures multiple humidity levels Thus, Gonzalez teaches acquiring measurement data under the claimed plurality of conditions. [3] “a quantitative information calculator configured to calculate a plurality of pieces of quantitative information relating to a substance based on the measurement data;” Gonzalez teaches: “Stage 2, (Sample Analysis): …Depending on the set APS protocol, the percentage of degradants or the drug loss at different time points and conditions will be quantified” (Fig. 11, page 15, par 1). Explanation Gonzalez calculates quantitative information from measurement data, including: degradation product concentration rate constants (k) reaction rates These values are: derived directly from measured data quantitatively characterize the substance (e.g., degradation behavior) Thus, the disclosed calculations correspond to the claimed quantitative information calculator. [4] “an estimator configured to estimate parameters of a reaction model based on the plurality of pieces of quantitative information;” Gonzalez teaches: “Stage 3 (Model Building and Stability Prediction): the data obtained in stage 2 will be used to build the corresponding degradation kinetic models. The degradation rate will be determined based on the theoretical model that better fits the experimental data points in all the conditions tested.” (Fig. 11, page 15, par 2) “Arrhenius equation… ln k = ln A − Ea/RT” (page 11) “parameters determined using nonlinear fitting programs” Explanation Gonzalez uses: Arrhenius or modified Arrhenius models fitted parameters such as: activation energy (Ea) pre-exponential factor (A) These parameters are: estimated from quantitative information (rate constants, degradation data) obtained via fitting procedures Thus, Gonzalez teaches an estimator that determines model parameters from calculated quantitative data. [5] “and a calculator configured to calculate at least one of: a value of the substance at a future time or a time at which the value reaches a threshold, based on the reaction model;” Gonzalez teaches: “predict the stability of products” (page 10) “a prediction of shelf life” (page 11) “extrapolating the Lnk at governs the degradation process at lower temperatures” (Fig. 9, page 11) Explanation Gonzalez explicitly performs: prediction of future values (e.g., degradation level at a future time) determination of time-to-threshold (shelf-life when impurity reaches limit) The Arrhenius-based extrapolation: uses fitted model parameters predicts behavior at future time/conditions Thus, Gonzalez teaches the claimed calculator. [6] “wherein the reaction model includes an integrated error term based on the plurality of analysis conditions including temperature and humidity.” Gonzalez teaches reaction modeling and fitting but does not explicitly disclose: an integrated error term within the reaction model explicit incorporation of error contributions across temperature and humidity conditions However, Waterman teaches: “Imprecision is incorporated into a Monte-Carlo simulation to propagate the variations inherent in the experiment” (abstract) “these data were fit with a multiple regression package… to determine… parameters” (page 783, par 0). Explanation (Gap Filled) Waterman discloses: statistical modeling of degradation data across multiple temperature and humidity conditions (page 788, par 2) propagation of error using Monte Carlo simulation (page 788, par 1). regression fitting incorporating variability in experimental data (page 783, par 0) The disclosed Monte Carlo and regression framework: aggregates error contributions from multiple datasets incorporates uncertainty arising from different conditions (temperature and humidity) integrates these errors into the parameter estimation and predictive model Thus, Waterman teaches a reaction modeling approach that includes an integrated error term based on multiple analysis conditions, as claimed. It would have been obvious to a person of ordinary skill in the art to modify Gonzalez in view of Waterman to incorporate integrated error modeling. Reason Gonzalez already performs: multi-condition modeling parameter estimation prediction of stability Gonzalez does not explicitly account for: uncertainty and variability across conditions Waterman provides this missing feature. Supporting Evidence Waterman teaches: “propagate the variations inherent in the experiment and provide error bars at extrapolated conditions.” (page 789, par 3). Technical Improvement Incorporating Waterman’s approach into Gonzalez would: improve accuracy of parameter estimation improve robustness of predictions account for experimental variability across temperature and humidity This results in: a more reliable predictive model reduced error in shelf-life estimation Conclusion of Rationale A person of ordinary skill would have been motivated to: combine Gonzalez’s modeling framework with Waterman’s statistical error integration to achieve improved predictive accuracy and robustness This combination yields: a reaction model including integrated error contributions across temperature and humidity conditions, as claimed. Regarding claim 2, as has been discussed regarding claim 1 above, Gonzalez discloses a sample reaction device (abstract), comprising 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 (Fig. 11, page 14, par 3); 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 (Fig. 11, page 15, par 1); an estimator that retrieves a reaction model stored in a storage device, 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 (Fig. 11, page 11, page 15, par 2); and 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 (Fig. 9, page 10-11). Gonzalez teaches multi-pathway degradation: “An organic compound in solution can undergo chemical reactions to give one or more products” (page 783, par 2). Here, Gonzalez explicitly teaches that degradation involves multiple reaction pathways different reactions may occur depending on conditions and composition This inherently suggests that: reaction models may include multiple reactions. Gonzalez does not explicitly state: setting an additional reaction based on an initial value. However, Waterman teaches: “there will be an initial rapid rate of reaction due to any reactive-form material present… followed by a slower rate… represented kinetically” (page 783, par 2). Here, Waterman discloses: multiple reaction pathways dependent on initial conditions (e.g., initial reactive-form concentration) modeling different reaction contributions depending on initial system state Thus, Waterman teaches setting additional reaction behavior based on initial value conditions, as claimed. Regarding claim 3, as has been discussed regarding claim 1 above, Gonzalez discloses a sample reaction device (abstract), comprising 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 (Fig. 11, page 14, par 3); 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 (Fig. 11, page 15, par 1); an estimator that retrieves a reaction model stored in a storage device, 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 (Fig. 11, page 11, page 15, par 2); and 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 (Fig. 9, page 10-11). Gonzalez does not explicitly disclose: a time difference in regard to start of an analysis is set in the reaction model. However, Waterman teaches: “initial rapid rate… followed by a slower rate” (page 783, par 2). This disclosure shows: reactions with different starting behaviors over time modeling systems where effective reaction start differs between components Such modeling inherently requires: accounting for time offsets or delayed reaction onset It would have been obvious to incorporate time-offset modeling into Gonzalez: In order to account for multi-phase reaction behavior, and improve accuracy of degradation modeling across conditions. Regarding claim 4, Gonzalez discloses that wherein an Arrhenius equation (Eq. 2) or a modified Arrhenius equation is applied to the reaction model (Eq. 3). Regarding claim 5, Gonzalez discloses that wherein light is included as the acceleration factor (page 1, par 1). Regarding claim 6, Gonzalez discloses that wherein a plurality of reaction models are stored in the storage device (page 13, par 1). Regarding claim 7, Gonzalez discloses that 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 (Table 4, page 13, par 1). Regarding claim 8, Gonzalez discloses that 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 (page 13, par 2). Regarding claim 9, Gonzalez discloses a pharmaceutical analysis device, wherein in the sample analysis device according to claim 1 (abstract), the sample includes a formulation or a drug substance, and the substance includes an active ingredient (API) or an impurity present in the formulation or the drug substance (page 1, par 1). Regarding claim 10, Gonzalez teaches a sample analysis method including: acquiring 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 (Fig. 11, page 14, par 3); calculating, based on the plurality of measurement data pieces, a plurality of quantitative measurement information pieces of a substance included in the sample (Fig. 11, page 15, par 1); retrieving a reaction model stored in a storage device, modelling quantitative estimation information of the substance with use of the reaction model, and providing the plurality of quantitative measurement information pieces to the reaction model to estimate a parameter of the reaction model (Fig. 11, page 15, par 2); and calculating, based on the estimated parameter, quantitative estimation information of the substance at an arbitrary point in time or calculating information in regard to a period of time until quantitative estimation information of the substance reaches a predetermined threshold value (page 10-11). Gonzalez does not specifically teach that 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. However, Waterman teaches: “Imprecision is incorporated into a Monte-Carlo simulation to propagate the variations inherent in the experiment” (abstract) “these data were fit with a multiple regression package… to determine… parameters” (page 783, par 0). Waterman discloses: statistical modeling of degradation data across multiple temperature and humidity conditions (page 788, par 2) propagation of error using Monte Carlo simulation (page 788, par 1). regression fitting incorporating variability in experimental data (page 783, par 0) The disclosed Monte Carlo and regression framework: aggregates error contributions from multiple datasets incorporates uncertainty arising from different conditions (temperature and humidity) integrates these errors into the parameter estimation and predictive model Thus, Waterman teaches 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, as claimed. It would have been obvious to a person of ordinary skill in the art to modify Gonzalez in view of Waterman to incorporate 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. Regarding claim 11, Gonzalez teaches a sample analysis method (abstract) including: acquiring 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 (Fig. 11, page 14, par 3); calculating, based on the plurality of measurement data pieces, a plurality of quantitative measurement information pieces of a substance included in the sample (Fig. 11, page 15, par 1); retrieving a reaction model stored in a storage device, modelling quantitative estimation information of the substance with use of the reaction model, and providing the plurality of quantitative measurement information pieces to the reaction model to estimate a parameter of the reaction model (Fig. 11, page 15, par 2); and calculating, based on the estimated parameter, quantitative estimation information of the substance at an arbitrary point in time or calculating information in regard to a period of time until quantitative estimation information of the substance reaches a predetermined threshold value (page 10-11). Gonzalez does not specifically teach that wherein an additional reaction is set in the reaction model in accordance with an initial value. However, However, Waterman teaches: “there will be an initial rapid rate of reaction due to any reactive-form material present… followed by a slower rate… represented kinetically” (page 783, par 2). Here, Waterman discloses: multiple reaction pathways dependent on initial conditions (e.g., initial reactive-form concentration) modeling different reaction contributions depending on initial system state Thus, Waterman teaches setting additional reaction behavior based on initial value conditions, as claimed. Regarding claim 12, Gonzalez teaches a sample analysis method (abstract) including: acquiring 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 (Fig. 11, page 14, par 3); calculating, based on the plurality of measurement data pieces, a plurality of quantitative measurement information pieces of a substance included in the sample (Fig. 11, page 15, par 1); retrieving a reaction model stored in a storage device, modelling quantitative estimation information of the substance with use of the reaction model, and providing the plurality of quantitative measurement information pieces to the reaction model to estimate a parameter of the reaction model (Fig. 11, page 15, par 2); and calculating, based on the estimated parameter, quantitative estimation information of the substance at an arbitrary point in time or calculating information in regard to a period of time until quantitative estimation information of the substance reaches a predetermined threshold value (page 10-11). Gonzalez does not specifically teach that wherein a time difference in regard to start of an analysis is set in the reaction model. However, Waterman teaches: “initial rapid rate… followed by a slower rate” (page 783, par 2). This disclosure shows: reactions with different starting behaviors over time modeling systems where effective reaction start differs between components Such modeling inherently requires: accounting for time offsets or delayed reaction onset It would have been obvious to incorporate time-offset modeling into Gonzalez: In order to account for multi-phase reaction behavior, and improve accuracy of degradation modeling across conditions. Regarding claim 13, Gonzalez teaches that 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 (page 1, par 1). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOYUN R XU, Ph. D. whose telephone number is (571)270-5560. The examiner can normally be reached M-F 8am-5pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lyle Alexander can be reached at 571-272-1254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /XIAOYUN R XU, Ph.D./Primary Examiner, Art Unit 1797 Application/Control Number: 18/517,981 Page 2 Art Unit: 1797 Application/Control Number: 18/517,981 Page 3 Art Unit: 1797 Application/Control Number: 18/517,981 Page 4 Art Unit: 1797 Application/Control Number: 18/517,981 Page 5 Art Unit: 1797 Application/Control Number: 18/517,981 Page 6 Art Unit: 1797 Application/Control Number: 18/517,981 Page 7 Art Unit: 1797 Application/Control Number: 18/517,981 Page 8 Art Unit: 1797 Application/Control Number: 18/517,981 Page 9 Art Unit: 1797 Application/Control Number: 18/517,981 Page 10 Art Unit: 1797 Application/Control Number: 18/517,981 Page 11 Art Unit: 1797 Application/Control Number: 18/517,981 Page 12 Art Unit: 1797 Application/Control Number: 18/517,981 Page 13 Art Unit: 1797 Application/Control Number: 18/517,981 Page 14 Art Unit: 1797
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