DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The rejections from the Office Action of 10/10/2025 are hereby withdrawn. New grounds for rejection are presented below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-9, and 11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 has been amended to recite that “the data processor is configured to draw a contour line of plots indicating values of the first factor and the second factor which are input into the regression model for outputting a specific prediction value” in the second-to-last element. This limitation is not supported by the originally-file Specification.
The dependent claims are rejected based on their dependence from Claim 1.
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.
Claim(s) 1, 5-9, and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyall et al. (US 20230385475 A1)[hereinafter “Lyall”], Sakurai et al. (US 20220410332 A1)[hereinafter “Sakurai”], and Ito et al. (US 20200103382 A1)[hereinafter “Ito”].
Regarding Claim 1, Lyall discloses a data analysis system [Figs. 1, 2, and 8] comprising a liquid chromatograph for obtaining analysis results [Fig. 1 and Paragraph [0031] – “As used herein, “chromatography” refers to a technique or process for separating an analyte into various components (e.g., molecules) of interest. An analyte is typically dissolved in a fluid (e.g., gas, solvent, water, etc.), which is generally referred to as the “mobile phase” or the carrier.”] and a data analysis system for analyzing the analysis result [Figs. 2 and 8], wherein the data analysis system comprising:
a data storage part [Fig. 8 – Storage Device 810] that stores a plurality of analysis results obtained by a plurality of chromatographic analyses performed by the liquid chromatograph under a plurality of analysis conditions and a plurality of parameters included in the analysis conditions [Abstract – “A method, system, and non-transitory computer readable medium for estimating mechanistic chromatography model uncertainty.”Paragraph [0047] – “In various embodiments, model analysis system 200 is used to generate mechanistic model 202 based on experiment data 212. Experiment data 212 may include, for example, data obtained or generated by performing one, two, three, or some other number of experiments. In one or more embodiments, experiment data 212 is generated using the data from about three to about ten experiments. In one or more embodiments, experiment data 212 is stored in data storage 206.”Paragraph [0048] – “Mechanistic model 202 includes a plurality of parameters 214. In various embodiments, at least a portion of parameters 214 may have actual physical meanings. In one or more embodiments, each of plurality of parameters 214 has an actual physical meaning that corresponds to the process of chromatography, fluid dynamics, mass transfer phenomena, or other properties or factors. In this manner, each of parameters 214 may provide a way of relating the output of mechanistic model 202 with the actual process of chromatography.”], wherein each analysis result is response and each analysis condition is factor, and the response and the factor are associated with each other [Paragraph [0051] – “Model analysis system 200 makes an assumption about the relationship between parameters 214. In various embodiments, model analysis system 200 makes an assumption that the underlying distribution is a multiparametric (multivariate) Gaussian distribution. This assumption is made to provide savings with respect to time, cost, processing resources, or a combination thereof. With this assumption, the range of values that is to be sampled from each of parameters 214 can be narrowed to improve the chances of sampling being performed for each of parameters 214 where the parameters, when looked at simultaneously, are most correlated. In other words, sampling is performed from the more likely values of parameters 214 based on the multiparametric (multivariate) Gaussian distribution.”Paragraph [0052] – “Based on this assumption, covariance matrix 220 may be computed at local extremum 218. Covariance matrix 220 describes a multiparametric (multivariate) Gaussian distribution across parameters 214 and helps identify the “narrowed space” from which parameters 214 may be sampled in order to reliably determine the uncertainty of mechanistic model 202.”See Fig. 6.];
a data processor configured to perform operation using data stored in the data storage part [Fig. 8 – Processor 804]; and
a display electrically connected to the data processor [Fig. 8 – Display 812], wherein
the data processor is configured to create a regression model [See Paragraphs [0047]-[0048]. Paragraph [0047] – “In various embodiments, model analysis system 200 is used to generate mechanistic model 202 based on experiment data 212.”] indicating a relationship of a variable with the response, by determining coefficients of each of terms constituting a predetermined model expression, in which the factor is the variable, based on the model expression by using a predetermined statistical analysis algorithm [Paragraph [0051] – “Model analysis system 200 makes an assumption about the relationship between parameters 214. In various embodiments, model analysis system 200 makes an assumption that the underlying distribution is a multiparametric (multivariate) Gaussian distribution. This assumption is made to provide savings with respect to time, cost, processing resources, or a combination thereof. With this assumption, the range of values that is to be sampled from each of parameters 214 can be narrowed to improve the chances of sampling being performed for each of parameters 214 where the parameters, when looked at simultaneously, are most correlated. In other words, sampling is performed from the more likely values of parameters 214 based on the multiparametric (multivariate) Gaussian distribution.”Paragraph [0052] – “Based on this assumption, covariance matrix 220 may be computed at local extremum 218. Covariance matrix 220 describes a multiparametric (multivariate) Gaussian distribution across parameters 214 and helps identify the “narrowed space” from which parameters 214 may be sampled in order to reliably determine the uncertainty of mechanistic model 202.”See Fig. 6.], wherein the regression model is for outputting a prediction value of the response based on the factor [Paragraph [0054] – “In various embodiments, model analysis system 200 displays an output generated based on uncertainty output 224 on display system. For example, uncertainty output 224 may include one or more confidence values (e.g., a 95% confidence value, a 5% and 95% confidence value, etc.). Model analysis system 200 may display an alert or a report on display system 208 that indicates whether these confidence values are acceptable (e.g., above or below a selected threshold).”], and
the data processor is configured to create reliability information, which is able to be referred by a user on the display, by quantifying reliability of the regression model based on a relationship with the response, the reliability information is variation of difference between the prediction value output from the regression model and the response [Paragraph [0053] – “Model analysis system 200 samples each of parameters 214 to form a plurality of simulation sets 222. Model analysis system 200 runs simulations of mechanistic model 202 using simulation sets 222 to generate various predictions using mechanistic model 202. These predictions are used to quantify an uncertainty for mechanistic model 202. For example, in one or more embodiments, the predictions are used to generate an uncertainty output 224 for mechanistic model 202. Uncertainty output 224 may include, for example, without limitation, an indication of the precision of mechanistic model 202. In one or more embodiments, uncertainty output 224 identifies a confidence interval or one or more confidence values for mechanistic model 202. For example, uncertainty output 224 may identify values for 95% confidence, 99.7% confidence, some other level of confidence, or a combination thereof. By providing information about the precision associated with mechanistic model 202, model analysis system 200 can provide confidence in mechanistic model 202.”Paragraph [0054] – “Uncertainty output 224 may take various forms. For example, uncertainty output 224 may be one or more values, a report containing a confidence interval, an alert identifying a confidence interval, a plot, some other type of visual representation of uncertainty, or a combination thereof. In one or more embodiments, model analysis system 200 displays uncertainty output 224 on display system 208. In various embodiments, model analysis system 200 displays an output generated based on uncertainty output 224 on display system. For example, uncertainty output 224 may include one or more confidence values (e.g., a 95% confidence value, a 5% and 95% confidence value, etc.). Model analysis system 200 may display an alert or a report on display system 208 that indicates whether these confidence values are acceptable (e.g., above or below a selected threshold).”],
the data processor is configured to create a two-dimensional graph in which a first scale axis indicates a first factor of the regression model and a second scale axis indicates a second factor of the regression model, and to draw a contour line of plots indicating values of the first factor and the second factor in the two-dimensional graph, and to display the two-dimensional graph with the contour line on the display, and the data processor is configured to display a range, in which the probability that the response matches the specific prediction value of the contour line is greater than or equal to a predetermined probability on the two-dimensional graph based on the variation [See the covariances 610/612 and their contour lines in Fig. 6.Paragraph [0053] – “In one or more embodiments, uncertainty output 224 identifies a confidence interval or one or more confidence values for mechanistic model 202. For example, uncertainty output 224 may identify values for 95% confidence, 99.7% confidence, some other level of confidence, or a combination thereof.”Paragraph [0072] – “Step 508 includes computing a covariance matrix for the mechanistic model based on the selected loss function. The covariance matrix describes the underlying multiparametric (multivariate) Gaussian distribution of the plurality of parameters.”Paragraph [0074] – “Further, first multiparametric plot 602 and second multiparametric plot 604 identify covariances 610 and covariances 612, respectively, between the parameters of the mechanistic model.”].
Lyall fails to disclose that the data processor is configured to draw a contour line of plots indicating values of the first factor and the second factor which are input into the regression model for outputting a specific prediction value in the two-dimensional graph; and the data processor is configured to display the prediction value and a range.
However, Sakurai discloses drawing a contour line of plots indicating values of a first factor and a second factor to evaluate a specific value in a two-dimensional graph and displaying the value and corresponding ranges [See two-dimensional map 331 in Figs. 3B and 7 which is used for determining anomalous data points T relative to boundaries (i.e., contour lines) 334, 335, and 336.]. It would have been obvious to evaluate model data pair predictions using such a strategy in order to evaluate the appropriateness of the model.
Lyall fails to disclose that each analysis result is the degree of separation of peaks in chromatogram, the number of peaks, or retention time of each peak, and each analysis condition is flow rate of a mobile phase, temperature of a column oven, composition of a mobile phase solvent, a mixing ratio of the mobile phase solvent, condition of gradient analysis, or a sample injection amount.
However, Ito discloses that the degree of separation of peaks in chromatogram and retention time of each peak are chromatography analysis results [Paragraph [0031] – “As the chromatographic performance indicator, for example, a Resolution, a separation factor, theoretical plate number, a peak area, a peak height, a peak width, a retention time, a holdup time, a retention factor, a height equivalent to a theoretical plate, a column pressure loss, a symmetry factor, a peak-valley ratio, an SN ratio, a baseline noise, a baseline drift, a limit of detection, a limit of quantitation, accuracy, and trueness can be applied.”] and that flow rate, temperature of a column, composition of a mobile phase solvent, a mixing ratio of the mobile phase solvent, condition of gradient analysis, and a sample injection amount are chromatography analysis conditions [Paragraph [0031] – “In particular, for example, a degree of a separation performance including at least one of the Resolution, the separation factor, the theoretical plate number, the height equivalent to a theoretical plate, and the peak width of the component can be applied. In addition, the apparatus setting parameters refer to setting of hardware, and for example, a gradient elution time program, an eluent switching timing, an eluent composition, a flow rate, an injection volume, a sample dilution magnification ratio, a column temperature, a column temperature switching timing, column selection, a reaction temperature, a reaction reagent composition, a detection wavelength, a detection wavelength switching timing, and a response time constant can be applied. In particular, for example, an elution condition including at least one of the eluent switching timing, the gradient elution time program, the column temperature switching timing, and the flow rate can be applied.”].
It would have been obvious to correlate these results/conditions in order to evaluate whether the chromatography process is performing properly.
Regarding Claim 5, Lyall discloses that the data processor is configured to display information on degree of contribution to the regression model of each of the terms included in the model expression on the display together with the reliability information [See the covariances 610/612 and their contour lines in Fig. 6.].
Regarding Claim 6, Lyall discloses that the reliability information includes validity information of the model expression based on deviation degree of the regression model with respect to each of the responses [See the covariances 610/612 and their contour lines in Fig. 6.Paragraph [0052] – “Covariance matrix 220 describes a multiparametric (multivariate) Gaussian distribution across parameters 214 and helps identify the “narrowed space” from which parameters 214 may be sampled in order to reliably determine the uncertainty of mechanistic model 202.”].
Regarding Claim 7, Lyall discloses that the statistical analysis algorithm is Bayesian inference [Paragraph [0051] – “Model analysis system 200 makes an assumption about the relationship between parameters 214. In various embodiments, model analysis system 200 makes an assumption that the underlying distribution is a multiparametric (multivariate) Gaussian distribution. This assumption is made to provide savings with respect to time, cost, processing resources, or a combination thereof. With this assumption, the range of values that is to be sampled from each of parameters 214 can be narrowed to improve the chances of sampling being performed for each of parameters 214 where the parameters, when looked at simultaneously, are most correlated. In other words, sampling is performed from the more likely values of parameters 214 based on the multiparametric (multivariate) Gaussian distribution.”Paragraph [0052] – “Based on this assumption, covariance matrix 220 may be computed at local extremum 218. Covariance matrix 220 describes a multiparametric (multivariate) Gaussian distribution across parameters 214 and helps identify the “narrowed space” from which parameters 214 may be sampled in order to reliably determine the uncertainty of mechanistic model 202.”].
Regarding Claim 8, Lyall discloses that the statistical analysis algorithm is a least squares method [Paragraph [0070] – “Step 504 includes selecting an initial parameter set from the plurality of parameter sets for the mechanistic model. For example, in step 504, the best-fitting parameter set may be used as the initial parameter set. In one or more embodiments, step 504 may be referred to as global optimization step and may be performed using, for example, any of a number of different types of loss functions. Examples of loss functions that can be used include, but are not limited to, a root-mean-square error (RMSE) algorithm, a maximum log-likelihood (or log-likelihood) algorithm, a negative log-likelihood algorithm, or a maximum likelihood algorithm.”].
Regarding Claim 9, Lyall discloses that the model expression is configured so as to be able to be optionally set by a user, and the arithmetic processor is configured to create the regression model based on the model expression set by a user [Paragraph [0070] – “Step 504 includes selecting an initial parameter set from the plurality of parameter sets for the mechanistic model. For example, in step 504, the best-fitting parameter set may be used as the initial parameter set. In one or more embodiments, step 504 may be referred to as global optimization step and may be performed using, for example, any of a number of different types of loss functions. Examples of loss functions that can be used include, but are not limited to, a root-mean-square error (RMSE) algorithm, a maximum log-likelihood (or log-likelihood) algorithm, a negative log-likelihood algorithm, or a maximum likelihood algorithm.”Paragraph [0071] – “Step 506 includes computing a local extremum for a selected loss function using the initial parameter set. In one or more embodiments, the selected loss function is a maximum log-likelihood (or log-likelihood), a negative log-likelihood, or a maximum likelihood algorithm. Thus, in some cases, the selected loss function used in step 506 may be the same or different from the loss function used in step 504. When the selected loss function is negative log-likelihood, the local extremum computed is the local minimum. When the selected loss function is maximum log-likelihood (or log-likelihood), the local extremum computed is the local maximum. When the selected loss function is maximum likelihood, the local extremum computed is the local maximum.”].
Regarding Claim 11, Lyall fails to disclose that the first factor and the second factor are analysis conditions.
However, Ito discloses that the degree of separation of peaks in chromatogram and retention time of each peak are chromatography analysis results [Paragraph [0031] – “As the chromatographic performance indicator, for example, a Resolution, a separation factor, theoretical plate number, a peak area, a peak height, a peak width, a retention time, a holdup time, a retention factor, a height equivalent to a theoretical plate, a column pressure loss, a symmetry factor, a peak-valley ratio, an SN ratio, a baseline noise, a baseline drift, a limit of detection, a limit of quantitation, accuracy, and trueness can be applied.”] and that flow rate, temperature of a column, composition of a mobile phase solvent, a mixing ratio of the mobile phase solvent, condition of gradient analysis, and a sample injection amount are chromatography analysis conditions [Paragraph [0031] – “In particular, for example, a degree of a separation performance including at least one of the Resolution, the separation factor, the theoretical plate number, the height equivalent to a theoretical plate, and the peak width of the component can be applied. In addition, the apparatus setting parameters refer to setting of hardware, and for example, a gradient elution time program, an eluent switching timing, an eluent composition, a flow rate, an injection volume, a sample dilution magnification ratio, a column temperature, a column temperature switching timing, column selection, a reaction temperature, a reaction reagent composition, a detection wavelength, a detection wavelength switching timing, and a response time constant can be applied. In particular, for example, an elution condition including at least one of the eluent switching timing, the gradient elution time program, the column temperature switching timing, and the flow rate can be applied.”].
It would have been obvious to correlate these results/conditions in order to evaluate whether the chromatography process is performing properly.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyall et al. (US 20230385475 A1)[hereinafter “Lyall”], Sakurai et al. (US 20220410332 A1)[hereinafter “Sakurai”], Ito et al. (US 20200103382 A1)[hereinafter “Ito”], and ccorti, Errors and contours of fit parameters, ROOT Forum, 9.2020 [hereinafter “ccorti”].
Regarding Claim 4, Lyall fails to disclose that the data processor is configured to allow a user to set the predetermined probability.
However, ccorti discloses programming that permits such functionality [See Pages 2-3, “s = 1, 2, or 3”:
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]. It would have been obvious to include such functionality in order to allow a user to readily view the covariance data [See Fig, 6 of Lyall] in a preferable manner.
Response to Arguments
Applicant argues:
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Examiner’s Response:
The Examiner agrees. However, Ito discloses the analysis of such analysis conditions. It would have been obvious to correlate these results/conditions in order to evaluate whether the chromatography process is performing properly.
Applicant argues:
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Examiner’s Response:
The Examiner agrees. However, Sakurai discloses analyzing specific parameter pairs relative to their likelihoods. It would have been obvious to evaluate model data pair predictions using such a strategy in order to evaluate the appropriateness of the model. New grounds for rejection are presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20220381751 A1 – METHOD FOR REALIZING MULTI-COLUMN CONTINUOUS FLOW CHROMATOGRAPHY DESIGN AND ANALYSIS
US 20180260726 A1 – ANALYSIS APPARATUS, ANALYSIS METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
US 20180060469 A1 – COMPARISON AND SELECTION OF EXPERIMENT DESIGNS
US 20170322190 A1 – Method And System For Determining The Influence Of Experimental Parameters On A Liquid Chromatography Protocol
US 20160367911 A1 – DETERMINATION OF CHROMATOGRAPHY CONDITIONS
US 20100292967 A2 – METHOD AND SYSTEM THAT OPTIMIZES MEAN PROCESS PERFORMANCE AND PROCESS ROBUSTNESS
US 20050086010 A1 – Stochastic Variable Selection Method For Model Selection
Borg et al., Effects of uncertainties in experimental conditions on the estimation of adsorption model parameters in preparative chromatography, Computers and Chemical Engineering, 2013
Yamamoto et al., Uncertainty quantification for chromatography model parameters by Bayesian inference using sequential Monte Carlo method, Chemical Engineering Research and Design, 9.10.2021
Ye et al., Design of experiment and data analysis by JMP, Journal of Pharmaceutical and Biomedical Analysis, 2000
US 20200103382 A1 – CHROMATOGRAPH
Miok, Estimation of Prediction Intervals in Neural Network-based Regression Models, IEEE 2018
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857