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 .
Status of the Claims
Claims 1-18 are currently pending and under exam herein.
Claims 1-18 are rejected.
Priority
The instant application claims priority from foreign application JP2021-147904 filed on 9/10/2021. Thus, the effective filing date of the instant application is 9/10/2021.
Drawings
The Drawings filed on 8/16/2022 were considered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/08/2024 and 5/14/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Claim Rejections - 35 USC § 101
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.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-17 are directed to a system and method of data analysis. Claim 18 is directed to a computer program. Under the broadest reasonable interpretation this can be stored as a transitory signal and is thus ineligible. Examiner will interpret as a non-transitory computer readable program for the purpose of compact prosecution.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
a variable setting step of causing a user to set at least one of the factors as the variable; (mathematical concept, mental process)
a structure setting step of causing a user to optionally set a structure of a model expression that is a basis of the approximate expression using the variable set in the variable setting step; a variable setting step of causing a user to set at least one of the factors as the variable; (mathematical concept, mental process)
a model expression determination step of determining the model expression based on the structure set by a user in the structure setting step; a variable setting step of causing a user to set at least one of the factors as the variable; (mathematical concept, mental process)
and an approximate expression determination step of determining a coefficient of each term constituting the model expression determined in the model expression determination step by regression analysis, and thereby determining the approximate expression a variable setting step of causing a user to set at least one of the factors as the variable; (mathematical concept, mental process)
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein a structure of the model expression includes at least one term of four arithmetic operations (mathematical concept)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the structure of the model expression includes at least one term of any one of a square root, a power, an exponential function, and a logarithmic function (mathematical concept)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the analysis is liquid chromatography analysis, the analysis result is any of number of peaks in a chromatogram, degree of separation of peaks in the chromatogram, and retention time of a peak appearing in the chromatogram, and the analysis condition includes, as the parameter, at least one of a type of one or more solvents constituting a mobile phase, a flow rate of each of the one or more solvents, a temperature of a separation column, and a sample injection amount (mathematical concept, this just limits the math)
Dependent claim 8 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the regression analysis is a least squares method (mathematical concept)
Dependent claim 9 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the regression analysis is Bayesian inference (mathematical concept)
Independent claim 10 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
A data analysis method comprising: an analysis data preparing step of preparing responses, which are a plurality of analysis results obtained by a plurality of analyses executed under a plurality of analysis conditions, and factors, which are a plurality of parameters included in the analysis conditions, in a state where the responses and the factors are associated with each other (mental process)
a variable setting step of optionally setting at least one of the factors as a variable (mathematical concept, mental process)
a structure setting step of optionally setting a structure of a model expression that is a basis of an approximate expression showing a relationship between the response and the variable by using the variable set in the variable setting step (mathematical concept, mental process)
a model expression determination step of determining the model expression based on the structure set in the structure setting step (mathematical concept, mental process)
and an approximate expression determination step of determining a coefficient of each term constituting the model expression determined in the model expression determination step by regression analysis, and thereby determining the approximate expression (mathematical concept, mental process)
Dependent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein in the structure setting step, a structure of the model expression is set using a structure and/or a term selected from a plurality of options for a structure of the model expression prepared in advance and/or a plurality of options for a term to be incorporated into the model expression prepared in advance (mental process, mathematical concept)
Dependent claim 12 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein a structure of the model expression that is optional is created in the structure setting step (mental process, mathematical concept)
Dependent claim 13 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the structure includes at least one term of four arithmetic operations (mathematical concept)
Dependent claim 14 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the structure includes at least one term of any of a square root, a power, an exponential function, and a logarithmic function (mathematical concept)
Dependent claim 15 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the analysis is liquid chromatography analysis, the analysis result is any of number of peaks in a chromatogram, degree of separation of peaks in the chromatogram, and retention time of a peak appearing in the chromatogram, and the analysis condition includes, as the parameter, at least one of a type of one or more solvents constituting a mobile phase, a flow rate of each of the one or more solvents, a temperature of a separation column, and a sample injection amount (mathematical concept, this just limits the math)
Dependent claim 16 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the regression analysis is a least squares method (mathematical concept)
Dependent claim 17 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the regression analysis is Bayesian inference (mathematical concept)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-18 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 1 includes:
A data analysis system comprising: a data storage part that stores responses, which are a plurality of analysis results obtained by a plurality of analyses executed under a plurality of analysis conditions, and factors, which are a plurality of parameters included in the analysis conditions, in a manner that the responses and the factors are associated with each other;
a data processor configured to use at least one of the factors as a variable and to create an approximate expression indicating a relationship between the variable and the responses;
and an information input device for a user to input information to the data processor, wherein the data processor is configured to execute
The additional element in dependent claim 2 includes:
a display electrically connected to the data processor, wherein in the structure setting step, the data processor is configured to display options of a structure of the model expression and/or options of a term to be incorporated into the model expression on the display, and to require a user to optionally select the options, thereby requiring the user to set a structure of the model expression.
The additional element in dependent claim 5 includes:
wherein the data processor is configured to be able to execute a model expression optional setting mode for a user to input a structure of the model expression that is optional in the structure setting step.
The additional element in dependent claim 6 includes:
wherein the data processor is configured to display a preview of a model expression of a structure set by a user on the display in the structure setting step.
The additional element in dependent claim 18 includes:
A computer program configured to execute the data analysis method according to claim 10 by being executed on a computer.
The additional elements of a data analysis system comprising: a data storage part that stores responses, which are a plurality of analysis results obtained by a plurality of analyses executed under a plurality of analysis conditions, and factors, which are a plurality of parameters included in the analysis conditions, in a manner that the responses and the factors are associated with each other (Claim 1), a data processor configured to use at least one of the factors as a variable and to create an approximate expression indicating a relationship between the variable and the responses (Claim 1), and an information input device for a user to input information to the data processor, wherein the data processor is configured to execute (Claim 1), a display electrically connected to the data processor, wherein in the structure setting step, the data processor is configured to display options of a structure of the model expression and/or options of a term to be incorporated into the model expression on the display, and to require a user to optionally select the options, thereby requiring the user to set a structure of the model expression (Clam 2), wherein the data processor is configured to be able to execute a model expression optional setting mode for a user to input a structure of the model expression that is optional in the structure setting step (Claim 5), wherein the data processor is configured to display a preview of a model expression of a structure set by a user on the display in the structure setting step (Claim 6), a computer program configured to execute the data analysis method according to claim 10 by being executed on a computer (Claim 18) ) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-18 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-18 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claims 1-18 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a data analysis system comprising: a data storage part that stores responses, which are a plurality of analysis results obtained by a plurality of analyses executed under a plurality of analysis conditions, and factors, which are a plurality of parameters included in the analysis conditions, in a manner that the responses and the factors are associated with each other (Claim 1), a data processor configured to use at least one of the factors as a variable and to create an approximate expression indicating a relationship between the variable and the responses (Claim 1), and an information input device for a user to input information to the data processor, wherein the data processor is configured to execute (Claim 1), a display electrically connected to the data processor, wherein in the structure setting step, the data processor is configured to display options of a structure of the model expression and/or options of a term to be incorporated into the model expression on the display, and to require a user to optionally select the options, thereby requiring the user to set a structure of the model expression (Clam 2), wherein the data processor is configured to be able to execute a model expression optional setting mode for a user to input a structure of the model expression that is optional in the structure setting step (Claim 5), wherein the data processor is configured to display a preview of a model expression of a structure set by a user on the display in the structure setting step (Claim 6), a computer program configured to execute the data analysis method according to claim 10 by being executed on a computer (Claim 18) ) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
When taken alone, all additional elements in claims 1-18 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-18 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Vera Candioti et al. (Vera Candioti, L.; De Zan, M. M.; Cámara, M. S.; Goicoechea, H. C. Experimental Design and Multiple Response Optimization. Using the Desirability Function in Analytical Methods Development. Talanta 2014, 124, 123–138.) in view of StatEase (Handbook for Experiments, StatEase, revised 4/9/2019) in further view of Lebrun et al. (Lebrun, P.; Boulanger, B.; Debrus, B.; Lambert, P.; Hubert, P. A Bayesian Design Space for Analytical Methods Based on Multivariate Models and Predictions. Journal of Biopharmaceutical Statistics 2013, 23 (6), 1330–1351). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claims 1, 3, 8, 10, 13, Vera Candioti et al. teaches a variable which can provide the necessary information in the evaluation of the analytical performance of the method must be selected to be subjected to the optimization procedure. This variable is called response and, according to the objective, it may be necessary to observe more than one response. Many variables may be selected as response for example, analyte recovery (accuracy), pre-concentration factor, peak area (sensibility), peak tailing, chromatographic resolution (selectivity), relative standard deviation (precision), migration or retention time (efficiency), etc. All the factors that may affect the process must be carefully detected and examined. The experimental domain must be defined for each factor and also a way of control and measurement must be established. The factors can be divided into quantitative, qualitative and mixture-related (e.g. volume of solvents). Since the number of factors to be considered can be important, it is necessary to perform screening experiments to determine the experimental variables and interactions that have a significant influence on one or several responses. In screening designs, the factors are usually examined at two levels (–1, þ1). The range between the levels is the broadest interval in which the factor can be varied for the system under study and is chosen on the basis of the literature information or earlier knowledge. (pg. 125, col. 2, paragraph 2-3, a data storage part that stores responses, which are a plurality of analysis results obtained by a plurality of analyses executed under a plurality of analysis conditions, and factors, which are a plurality of parameters included in the analysis conditions, in a manner that the responses and the factors are associated with each other; (Claim 1), A data analysis method comprising: an analysis data preparing step of preparing responses, which are a plurality of analysis results obtained by a plurality of analyses executed under a plurality of analysis conditions, and factors, which are a plurality of parameters included in the analysis conditions, in a state where the responses and the factors are associated with each other (claim 10) Vera Candioti et al. teaches once the data corresponding to the responses evaluated in the optimization stage have been collected, a mathematical model can be built for each response fitting a second order polynomial function. The general equation used for this purpose is the following:
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The model equation is usually fitted by the least square (LS) methodology, a multiple regression technique that fits a model to set the experimental data finding coefficients values that minimize the residual term ε. (pg. 128, col. 1 pargraph 4 – col. 2 paragraph 1, a data processor configured to use at least one of the factors as a variable and to create an approximate expression indicating a relationship between the variable and the responses (Claim 1), and an approximate expression determination step of determining a coefficient of each term constituting the model expression determined in the model expression determination step by regression analysis, and thereby determining the approximate expression. (Claim 1), wherein a structure of the model expression includes at least one term of four arithmetic operation (Claim 3), wherein the regression analysis is a least squares method (claim 8), wherein the structure includes at least one term of four arithmetic operations (Claim 13), wherein the regression analysis is a least squares method (Claim 16)
With respect to the limitations of Claims 7, 15, Vera Candioti et al. teaches many variables may be selected as response for example, analyte recovery (accuracy), pre-concentration factor, peak area (sensibility), peak tailing, chromatographic resolution (selectivity), relative standard deviation (precision), migration or retention time (efficiency), etc. (pg. 125, col. 2, paragraph 2, wherein the analysis is liquid chromatography analysis, the analysis result is any of number of peaks in a chromatogram, degree of separation of peaks in the chromatogram, and retention time of a peak appearing in the chromatogram, and the analysis condition includes, as the parameter, at least one of a type of one or more solvents constituting a mobile phase, a flow rate of each of the one or more solvents, a temperature of a separation column, and a sample injection amount (Claim 7), wherein the analysis is liquid chromatography analysis, the analysis result is any of number of peaks in a chromatogram, degree of separation of peaks in the chromatogram, and retention time of a peak appearing in the chromatogram, and the analysis condition includes, as the parameter, at least one of a type of one or more solvents constituting a mobile phase, a flow rate of each of the one or more solvents, a temperature of a separation column, and a sample injection amount (Claim 15)
Vera Candioti et al. does not explicitly teach
A data analysis system comprising: (Claim 1)
a model expression determination step of determining the model expression based on the structure set by a user in the structure setting step (Claim 1),
a structure setting step of causing a user to optionally set a structure of a model expression that is a basis of the approximate expression using the variable set in the variable setting step, (Claim 1),
and an information input device for a user to input information to the data processor, wherein the data processor is configured to execute: a variable setting step of causing a user to set at least one of the factors as the variable (Claim 1)
a display electrically connected to the data processor, wherein in the structure setting step, the data processor is configured to display options of a structure of the model expression and/or options of a term to be incorporated into the model expression on the display, and to require a user to optionally select the options, thereby requiring the user to set a structure of the model expression (Claim 2)
wherein the structure of the model expression includes at least one term of any one of a square root, a power, an exponential function, and a logarithmic function (Claim 4)
wherein the data processor is configured to be able to execute a model expression optional setting mode for a user to input a structure of the model expression that is optional in the structure setting step (Claim 5)
wherein the data processor is configured to display a preview of a model expression of a structure set by a user on the display in the structure setting step (Claim 6)
wherein the regression analysis is Bayesian inference (Claim 9)
a model expression determination step of determining the model expression based on the structure set in the structure setting step (Claim 10))
and an approximate expression determination step of determining a coefficient of each term constituting the model expression determined in the model expression determination step by regression analysis, and thereby determining the approximate expression (Claim 10)
a variable setting step of optionally setting at least one of the factors as a variable; a structure setting step of optionally setting a structure of a model expression that is a basis of an approximate expression showing a relationship between the response and the variable by using the variable set in the variable setting step (Claim 10)
wherein in the structure setting step, a structure of the model expression is set using a structure and/or a term selected from a plurality of options for a structure of the model expression prepared in advance and/or a plurality of options for a term to be incorporated into the model expression prepared in advance (Claim 11))
wherein a structure of the model expression that is optional is created in the structure setting step (Claim 12)wherein the structure includes at least one term of any of a square root, a power, an exponential function, and a logarithmic function (Claim 14)
wherein the regression analysis is Bayesian inference (Claim 17)
A computer program configured to execute the data analysis method according to claim 10 by being executed on a computer (Claim 18)
With respect to the limitations of Claims 1, 10, 12, 18, StatEase teaches a GUI on a computer which is used for an information input device for a user to input information to the data processor. StatEase asks the user to select variables for analysis (pg. 10) and Examine the F tests to determine if the complexity of the polynomial be reduced. Look for terms that can be eliminated, i.e., coefficients having p-values > 0.10. Be sure to maintain hierarchy (pg. 36, and an information input device for a user to input information to the data processor, wherein the data processor is configured to execute: a variable setting step of causing a user to set at least one of the factors as the variable (Claim 1) a variable setting step of optionally setting at least one of the factors as a variable; a structure setting step of optionally setting a structure of a model expression that is a basis of an approximate expression showing a relationship between the response and the variable by using the variable set in the variable setting step (Claim 10) wherein a structure of the model expression that is optional is created in the structure setting step (Claim 12), A computer program configured to execute the data analysis method according to claim 10 by being executed on a computer (Claim 18) StatEase also teaches to a model based on subject matter knowledge of the relationship between factors and responses (pg. 10, a structure setting step of causing a user to optionally set a structure of a model expression that is a basis of the approximate expression using the variable set in the variable setting step, (Claim 1), a model expression determination step of determining the model expression based on the structure set in the structure setting step (Claim 10)) StatEase also teaches the user picking among different polynomial models offered, once selected a specific equation is built for that polynomial. The regression fitting is equivalent to the model expression determination step (pg. 36, Response Surface/Mixture Analysis Guide, a model expression determination step of determining the model expression based on the structure set by a user in the structure setting step (Claim 1), and an approximate expression determination step of determining a coefficient of each term constituting the model expression determined in the model expression determination step by regression analysis, and thereby determining the approximate expression (Claim 10)).
With respect to the limitations of Claims 2, 6, StatEase teaches a GUI on a computer for displaying results including model expression of s structure set, the half-normal plots, Pareto chart, and automatic model selection menus where the user has an ability to select which variables to use. Both of these interactions require the user to select options (pg. 34, Factor Analysis Guide, a display electrically connected to the data processor, wherein in the structure setting step, the data processor is configured to display options of a structure of the model expression and/or options of a term to be incorporated into the model expression on the display, and to require a user to optionally select the options, thereby requiring the user to set a structure of the model expression (Claim 2), wherein the data processor is configured to display a preview of a model expression of a structure set by a user on the display in the structure setting step (Claim 6)
With respect to the limitations of Claims 4, 14, StatEase teaches different response transformations that can be used in their models including square root, exponential, and logarithmic functions (pg. 50, wherein the structure of the model expression includes at least one term of any one of a square root, a power, an exponential function, and a logarithmic function (Claim 4) wherein the structure includes at least one term of any of a square root, a power, an exponential function, and a logarithmic function (Claim 14)
With respect to the limitations of Claims 5, StatEase teaches a custom mode where user inputs an optional user specified polynomial model (pg. 26, Custom Design Selection, wherein the data processor is configured to be able to execute a model expression optional setting mode for a user to input a structure of the model expression that is optional in the structure setting step (Claim 5))
With respect to the limitations of Claims 11, StatEase teaches to specify degree “m” of polynomial (1 - linear, 2 - quadratic or 3 - cubic). Design is then constructed of m+1 equally spaced values from 0 to 1 (coded levels of individual mixture component). The resulting number of blends depends on both the number of components (“q”) and the degree of the polynomial “m”. Centroid not necessarily part of design. The handbook is explicit about different prepared polynomial structures the user can choose (pg. 25, Mixture Design Selection, first bullet point, wherein in the structure setting step, a structure of the model expression is set using a structure and/or a term selected from a plurality of options for a structure of the model expression prepared in advance and/or a plurality of options for a term to be incorporated into the model expression prepared in advance (Claim 11))
With respect to the limitations of Claims 1, 9, 17, Lebrun et al. teaches a data analysis system that includes Bayesian inference for HPLC analysis (pg. 2, paragraphs 2-3, A data analysis system comprising: (Claim 1), wherein the regression analysis is Bayesian inference (Claim 9,) wherein the regression analysis is Bayesian inference (Claim 17)).
A person of ordinary skill in the art would be motivated to combine Vera Candioti et al. with the StateEase Guidebook as Vera Candioti et al. describes modeling HPLC data and the StateEase Guidebook just provides a general statistical framework for analyzing any type of data including HPLC data. A person of reasonable skill in the art would also be motivated to combine Lebrun et al. as it provides additional information on HPLC modeling using Bayesian inference. All the prior art used directly addresses the problem of using statistics to analyze data. There is a reasonable expectation of success because each technique works separately and when combining different methods none of the underlying statistical methods change. So, they are expected to continue to function together as they did separately. In addition, there is a reasonable expectation of success, because the combination requires no new techniques and just standard application of already published and well-known statistical methods to HPLC.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Connor Beveridge whose telephone number is 571-272-2099. The examiner can normally be reached Monday - Thursday 9 am - 5 pm.
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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