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 .
Response to Arguments
Applicant’s arguments filed 12 December 2025, have been fully considered. Claims 1-6 and 11-13 are pending, all of which have been amended; claims 7-10 and 14-15 are canceled.
All claim objections are withdrawn in response to the applicant’s amendments.
Applicant’s efforts to amend the claims to address the 112(b) rejections have been considered and are satisfactory, however new grounds of rejection are given in light of the amendments. See 112(b) rejections below.
Applicant’s arguments concerning eligibility under 35 U.S.C. 101 have been considered but are not satisfactory.
The applicant argues at Step 2A, Prong One, that although the independent claims may be based on mathematical operations, they do not recite the mathematical concepts themselves and are therefore not directed to mathematical relationships, formulas, or calculations. The examiner disagrees. Performing Bayesian estimation of parameters in a model and outputting posterior predictive distributions both describe mathematical processes involving calculations. For example, although the claim language does not recite in great detail what operations are performed in order to produce a posterior predictive distribution from measured waveform data, the process of taking data and producing a statistical distribution of aspects of the data would describe a set of mathematical operations when given broadest reasonable interpretation.
The applicant further argues at Step 2A, Prong Two, that the claims are directed to the practical application of analyzing a waveform included in a chromatograph, and determining a ratio of peak areas between peak shapes in order to determine an impurity concentration.
The examiner disagrees that the claims are directed to a practical application. Nowhere in the claim language is the device limited to a chromatograph, nor the measured waveform to data obtained by a chromatograph. In fact, the nature of the sample, device, and waveform are unspecified. While chromatography is certainly one field to which the claim language could apply, there may be many other fields of application. Therefore the claim language does not integrate the judicial exceptions into some practical, concrete application. See 101 rejections below.
Applicant’s arguments concerning the prior art rejections under 35 U.S.C. 103 have been considered but are not satisfactory.
The applicant states that the examiner acknowledges that Golshan does not disclose the claimed first and second predictive distributions, nor performing Bayesian estimation. As amended, Golshan states that the (posterior) predictive distributions are conditioned on the measured waveform and that the quantitative indicator is a ratio of peak areas between peak shapes. The applicant argues that Denny does not address the above because Denny, while teaching use of Bayesian tools, does not disclose a posterior predictive distribution conditioned on a measured waveform, nor quantitative indicators such as impurity peak area ratios, nor quantifying uncertainty due to overlapping peaks. The applicant argues that Yanagisawa does not use Bayesian estimation for peak shape parameters, or evaluate uncertainty of impurity concentration, or display multiple distributions with a configurable display mode.
The examiner agrees that Golshan does not disclose the first and second predictive distributions. The examiner relied on Yanagisawa to argue that generating predictive distributions of peak shape and peak height would be useful for determining a probability distribution of an amount of a substance (Yanagisawa, ¶2: “the concentration and/or quantity of [a] substance is calculated from the height or area of the peak”), and thus offers one motivation for generating first and second predictive distributions. The examiner also used Yanagisawa to motivate determining a concentration ratio between two components for impurity analysis (Yanagisawa, Abstract and ¶6: “One of the applications of liquid chromatographs is an impurity analysis for analyzing the proportion of an impurity relative to a principal component.”). Therefore it would also be reasonable to generate a distribution of impurity peak area ratios, in order to get an estimate of the level of impurity in a measured waveform of interest.
While Golshan does not describe measuring a waveform, it was argued that it would have been obvious to do so, because Golshan is a scientific study directed to improving methods for obtaining component concentrations when spectra (such as chromatographic spectra) comprise overlapping substances.
Denny was relied upon to show that Bayesian estimation may be used for peak shape estimation models with multiple overlapping peaks. Any form of Bayesian statistics often results in a predictive distribution which is the solution to Bayes’ Theorem; this distribution is typically termed a “posterior distribution.” Thus it would be reasonable to conclude that Golshan, incorporating teachings from Denny and using Bayesian estimation, would end up generating a posterior predictive distribution which is conditioned on the measured waveform.
Finally, the uncertainty in the predictive component peak distributions is quantified in Golshan (Golshan, Fig. S1, see plots below the top of column 2). This uncertainty is due to the fact that the original data represents overlapping peaks (see rejection of claim 1). See 103 rejections below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-6 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites “a first posterior predictive distribution” and “a second posterior predictive distribution,” which is confusing because claim 1 already recites first and second posterior predictive distributions. It is therefore unclear whether claim 3 is referring to different first and second posterior predictive distributions. Since much of claim 3 is copied from the text of claim 1, the examiner assumes that the applicant intends the above distributions to refer to the same distributions described in claim 1, and that claim 3 is meant to claim that the steps of outputting the “first” and “second” distributions, and of changing “a display mode,” are performed in response to “a user’s selection received by the input receiver.” The examiner recommends replacing “a” with “the” for both distributions above, and will use this interpretation for examination purposes.
Claim 13 contains the same issue as claim 3, is rejected for the same reasons, and will be interpreted the same way as described above for examination purposes.
Claim 4 depends from claim 2 then recites “the threshold received by the input receiver.” This is confusing because it refers to a threshold received by an input receiver, however the only threshold previously recited was given in claim 2, and that threshold is not required to be received by an input receiver. Therefore it is unclear whether the threshold referred to is a different threshold. The examiner assumes that the applicant intends for both to refer to the same threshold; if this is the case, the examiner recommends rewriting claim 4 to read:
The analysis device according to claim 2, wherein the data processing device further comprises an input receiver that receives a user's input, [[and]]
wherein the threshold is received by the input receiver, and
wherein the display processor is operable to display, as the quantile, the value of the quantitative indicator at which there is a probability equal to the threshold
This will be the interpretation used for examination purposes.
Claim 5 contains the same issue as claim 4, is rejected for the same reasons, and will be interpreted the same way as described above for examination purposes.
Claim 6 depends from claim 5, therefore it inherits the same issues and is rejected for the same reasons.
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 1-6 and 11-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At Step 1 of the 101 analysis, all claims are directed to one of the statutory categories of invention.
Claim 1 is rejected in response to the following analysis:
At Step 2A, Prong One, the judicial exceptions are bolded in the copy of claim 1 below:
An analysis device that performs an analysis of a sample, the analysis device comprising:
a measurement unit that performs a measurement of the sample and outputs a measured waveform obtained by the measurement of the sample; and
a data processing device that performs a data process on the measured waveform, the data processing device comprising:
an estimator that performs Bayesian estimation of peak shape parameters in a peak shape model for a plurality of peak waveforms included in the measured waveform and closely appearing to each other, and outputs, for each of the plurality of peak waveforms, a posterior predictive distribution of a peak shape, the posterior predictive distribution being conditioned on the measured waveform and obtained by the Bayesian estimation of the peak shape parameters, the Bayesian estimation determining an uncertainty of the peak shapes caused by superposition of the plurality of peak waveforms;
a calculator that outputs, based on the outputted posterior predictive distribution of the peak shape from the estimator, a posterior predictive distribution of a quantitative indicator for each of the plurality of peak waveforms, the posterior predictive distribution of the quantitative indicator being conditioned on the measured waveform and obtained by the Bayesian estimation of the peak shape parameters, the quantitative indicator being a ratio of peak areas between peak shapes for determining an impurity concentration; and
a display processor operable to display the outputted posterior predictive distribution of the quantitative indicator from the calculator,
wherein the calculator outputs, based on the outputted posterior predictive distribution of the peak shape from the estimator, a first posterior predictive distribution and a second posterior predictive distribution for each of the plurality of peak waveforms, the first posterior predictive distribution and the second posterior predictive distribution being related to each other and being based on the quantitative indicator, and
wherein the display processor is operable to change a display mode of the first posterior predictive distribution and the second posterior predictive distribution.
Performing Bayesian estimation of parameters in a model for peaks in a measured waveform and outputting predictive distributions of peak shapes and quantitative indicators all describe mathematical operations.
At Step 2A, Prong Two, the additional elements include an analysis device which comprises a measurement unit, a data processing device, a calculator, and a display processor. Most of these elements are encompassed by a general-purpose computer, and without further limitations the “measurement unit” can be interpreted broadly to encompass a number of fields. Other additional elements describe measuring a sample and outputting a measured waveform, displaying predictive distributions, and changing a display mode. The sample measurement describes necessary data gathering, and the display functionality is insignificant extra-solution activity. When considered together, these additional elements do not indicate a specific set of operations performed by a particular machine on a sample for a particular, practical purpose. For these reasons, the additional elements do integrate the judicial exceptions into a practical application.
At Step 2B, when considered as a whole, claim 1 does not amount to significantly more than the judicial exceptions for the reasons given above.
Claims 2-6 describe further mathematical operations including calculating a quantile at some threshold for a statistical distribution, and indicate that user input to an input receiver can cause the operations to occur or change display settings. These limitations do not remedy the issues described in the analysis of claim 1, therefore these claims are also rejected.
Claim 11 is a method claim which recites the data processing method recited in claim 1, therefore claim 11 is rejected for the same reasons.
Claims 12 and 13 recite the methods detailed in claims 2 and 3, respectively, and are rejected for the same reasons.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Golshan (“A review of recent methods for the determination of ranges of feasible solutions resulting from soft modelling analyses of multivariate data”) in view of Denny (US 20160217986 A1) and Yanagisawa (US 20150308992 A1).
Regarding claim 1, Golshan discloses an analysis device (a computer performs the numerical methods and plots the results) that performs an analysis of a simulated waveform (see Fig. S1 in the “Electronic Supporting Information”; the simulated waveforms are in the top row, second and fourth column (chromatographic and spectral data, respectively, of a three-component chromatographic dataset)), the analysis device comprising:
a data processing device (the computer is also a data processing device) that performs a data process on a waveform (Fig. S1, the simulated chromatographic waveform is a superposition of the peaks in the top of column 2, and data processing is performed to separate the peaks. While Golshan only ever displays the true peaks independently, as in Fig. S1, it is clear that the method works by receiving the simulated waveform as a superposition of peaks (see D in Eq. 1) and working to decompose the simulated waveform into individual component peaks; see pg. 2, Eq. 1, and final paragraph: “Given the measurement D, the goal of soft modelling analysis is the determination of the two product matrices C and A.” A gives the molar absorption spectra of each component, and C gives the concentration of each component), the data processing device comprising:
an estimator (the computer) that performs estimation of peak shape parameters in a peak shape model (Fig. S1, the Borgen-Rajko, Simplex, Polygon inflation, and MCR-BANDS are all models which estimate parameters related to peak shapes) for a plurality of peak waveforms (Fig. S1, top of column 2: three peak waveforms are indicated) included in the waveform (see above describing how the simulated waveform is received as a superposition of peaks) and closely appearing to each other (see top of column 2 in Fig. S1; the peaks appear close to each other), and outputs, for each of the plurality of peak waveforms, a predictive distribution of a peak shape (Fig. S1, column two, below the top image is displayed a predictive distribution of peak shapes for each of three peaks), the predictive distribution being conditioned on the waveform and obtained by the estimation of the peak shape parameters (Fig. S1, the predictive distributions below the top image of column two are conditioned on the received simulated waveform (again, refer to matrix D in Eq. 1) and obtained by the estimations of each of the models), the estimation determining an uncertainty of the peak shapes caused by superposition of the plurality of peak waveforms (Fig. S1, the shaded regions represent uncertainties in peak shapes; these uncertainties are a result of the nature of received spectra, which superimpose the contributions of individual components at given wavelengths);
a calculator (the computer is also a calculator); and
a display processor (the computer displays graphs on a display).
Golshan does not explicitly disclose that the analysis device performs analysis of a sample, and that the waveform was measured by a measurement unit that performs a measurement of the sample and outputs a measured waveform obtained by measurement of the sample. However, the purpose of Golshan’s review paper is to compare methods for use in separating actual three-component systems (Abstract: “The purpose of the current review is to describe and critically compare the available methods that attempt at determining the range of ambiguity for the case of 3-component systems.”), therefore it would have been obvious to apply the method of device of Golshan to perform a data process on a measured waveform obtained by measuring a sample.
Golshan does not explicitly disclose that the estimation uses Bayesian statistics.
Denny teaches a method of modeling overlapping mass spectral peaks (¶78: “The preferred embodiment is able to detect defects in an experimentally obtained mass spectrum by modelling the effects of defects on the spectral data, and by comparing the modelled data to the experimentally obtained data.” Fig. 4B and ¶81 discuss an observed spectrum w which is modeled by z = x + y, where x is a first spectrum consisting of multiple isotope peaks, and y is a second spectrum representing a shifted and diluted form of x as a result of a defect), and further teaches that Bayesian analysis can be used to model peak shapes (¶97: “Bayesian analysis may be used in order to determine which configurations of model for the peak shapes are more likely to be correct.”). Denny further teaches that Bayesian analysis can incorporate previously known information (¶97: “Bayesian analysis combines what was known before the data were inspected with what information the data provides in a coherent manner.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Denny with the invention of Golshan by causing the estimation to use Bayesian methods. Doing so would enable one to incorporate prior information into a statistical model. Having done so, it would be reasonable to cause the predictive distributions to be “posterior” distributions, as they represent the conditional probability of some parameter given that some other condition is true.
Golshan in view of Denny does not explicitly disclose:
that the calculator outputs, based on the outputted posterior predictive distribution of the peak shape from the estimator, a posterior predictive distribution of a quantitative indicator for each of the plurality of peak waveforms, the posterior predictive distribution of the quantitative indicator being conditioned on the measured waveform and obtained by the Bayesian estimation of the peak shape parameters, the quantitative indicator being a ratio of peak areas between peak shapes for determining an impurity concentration; and
that the display processor is operable to display the outputted posterior predictive distribution of the quantitative indicator from the calculator,
wherein the calculator outputs, based on the outputted posterior predictive distribution of the peak shape from the estimator, a first posterior predictive distribution and a second posterior predictive distribution for each of the plurality of peak waveforms, the first posterior predictive distribution and the second posterior predictive distribution being related to each other and being based on the quantitative indicator, and
wherein the display processor is operable to change a display mode of the first posterior predictive distribution and the second posterior predictive distribution.
Yanagisawa teaches a data processing system to estimate a concentration ratio between two components (Abstract). Yanagisawa further teaches that the height and area of a chromatogram peak are proportional to the amount of the corresponding substance (¶2: “the concentration and/or quantity of [a] substance is calculated from the height or area of the peak”). Yanagisawa also teaches that knowing the relative concentration of one component to another can be important, for example in impurity analysis (¶6: “One of the applications of liquid chromatographs is an impurity analysis for analyzing the proportion of an impurity relative to a principal component. For example, impurity analyses are frequently performed for drugs or similar products.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Yanagisawa with the invention of Golshan in view of Denny by causing the calculator to output, based on the outputted posterior predictive distribution of the peak shape from the estimator, a posterior predictive distribution of a quantitative indicator for each of the plurality of peak waveforms, the posterior predictive distribution of the quantitative indicator being conditioned on the measured waveform and obtained by the Bayesian estimation of the peak shape parameters, the quantitative indicator being a ratio of peak areas between peak shapes for determining an impurity concentration; and
causing the display processor to be operable to display the outputted posterior predictive distribution of the quantitative indicator from the calculator,
wherein the calculator outputs, based on the outputted posterior predictive distribution of the peak shape from the estimator, a first posterior predictive distribution (peak height or area) and a second posterior predictive distribution (peak height or area) for each of the plurality of peak waveforms, the first posterior predictive distribution and the second posterior predictive distribution being related to each other and being based on the quantitative indicator.
Calculating a predictive distribution of a ratio of peak areas for each peak waveform would be beneficial to one wishing to estimate the ratio of the amount of one substance to one or more other substances. This would be useful for example in impurity analysis to estimate the concentration of an impurity relative to a principal component. Such a distribution would be calculable from the predictive distributions of peak area for individual waveforms, for example, and so would be based on the outputted posterior predictive distribution of the peak shape (since the peak area distributions themselves are derived from the peak shape distributions).
In a chromatogram, the intensity at a given time represents the concentration of one or more components eluting at that moment, while the area of a waveform represents the total amount of the components eluted. Therefore, calculating a peak area predictive distribution from each peak waveform would be useful in order to obtain a predictive distribution proportional to total amount for each substance. Furthermore, it would have been obvious to derive a peak height predictive distribution from each shape predictive distribution, as a secondary way of estimating the predictive distribution of amount for each substance. Both distributions ought to be proportional to component amount, therefore any differences not attributable to a scale factor would provide useful information, such as information about random noise (random noise would affect peak height more than peak area, since peak area occurs over an extended time period).
In light of the above, it would have been obvious to one of ordinary skill in the art practicing the invention of Golshan in view of Denny and Yanagisawa to cause the display processor to be operable to change a display mode of the first posterior predictive distribution and the second posterior predictive distribution. Doing so would enable one to display desired predictive distributions with desired settings.
Regarding claim 3, claim 3 recites the same limitations as claim 1 and is rejected for largely the same reasons. Claim 3 also recites that the data processing device further comprises an input receiver that receives a user’s input, and that the steps of outputting the first and second posterior predictive distributions and of changing a display mode are performed based on a user’s selection received by the input receiver. It would have been obvious for one of ordinary skill in the art practicing the invention of Golshan in view of Denny and Yanagisawa to include these limitations in order to enable a user to interact with graphics displayed on a screen and display any distributions they are interested in.
Regarding claim 11, claim 11 describes the method performed by the analysis device in claim 1, and contains the same limitations as given in claim 1. Claim 11 is therefore rejected for the same reasons.
Regarding claim 13, claim 13 describes the limitations applied by the analysis device at claim 3 and is therefore rejected for the same reasons.
Claims 2, 4-6, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Golshan (“A review of recent methods for the determination of ranges of feasible solutions resulting from soft modelling analyses of multivariate data”) in view of Denny (US 20160217986 A1) and Yanagisawa (US 20150308992 A1), and further in view of Walpole (“Probability and Statistics for Engineers and Scientists: Ninth Edition”).
Regarding claim 2, Golshan in view of Denny and Yanagisawa teaches the limitations of claim 1 but does not teach the limitations of claim 2.
Consider a scenario where one considers the posterior predictive distribution of quantitative indicator (see rejection of claim 1). One would reasonably be interested in determining how confidently one can assume that the impurity is sufficiently low relative to the principal component; in other words, how confidently one can assume that the quantitative indicator is below some level. Let the impurity ratio be symbolized by a quantile
Q
.
Walpole teaches the use of probability density functions (PDFs) to describe the probability of a random variable being observed to be within a certain range (pg. 89, Definition 3.6). Walpole teaches that, given a PDF
f
(
x
)
, the probability that the random variable it describes is within a range
[
a
,
b
]
is given by
∫
a
b
f
(
x
)
d
x
(pg. 89, Definition 3.6, see point 3.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Walpole with the invention of Golshan in view of Denny and Yanagisawa by causing the predictive distribution of the ratio described above to be a PDF to take advantage of the properties of PDFs.
Call the predictive distribution of the ratio
f
(
x
)
and let the quantitative indicator be represented by
I
. To determine the confidence that
I
is no greater than
Q
, one could calculate
T
=
P
I
≤
Q
=
∫
0
Q
f
x
d
x
. Using the PDF properties defined in Walpole, the threshold
T
is the percent probability that the impurity is less than or equal to
Q
.
Just as one testing for impurities would be motivated to set a fixed impurity ratio and calculate their confidence that the ratio is not exceeded, as above, one would also be motivated to fix a confidence level and calculate the ratio that is not exceeded at that confidence level. Fixing a confidence level is a common way of doing statistics (for example see Walpole, pg. 274, Example 9.4: “Give an upper 95% bound for the mean reaction time.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Walpole with the invention of Golshan in view of Denny and Yanagisawa by causing the calculator to calculate a quantile at a threshold in the outputted posterior predictive distribution of the quantitative indicator, and to cause the display processor to be operable to display the quantile calculated by the calculator. This would enable one to perform statistical tests as described above.
Regarding claim 4, Golshan in view of Denny and Yanagisawa and Walpole teaches the limitations of claim 2. Furthermore, it would have been obvious to cause the data processing device to further comprise an input receiver that receives a user’s input for the reasons given in the rejection of claim 3 (see rejection of claim 3).
Referring to the test in claim 2 (see rejection of claim 2), it would have been obvious to one of ordinary skill in the art practicing the invention of Golshan in view of Denny and Yanagisawa and Walpole to cause the display processor to be operable to display, as the quantile, the value of the quantitative indicator at which there is a probability equal to the threshold that the true value of the quantitative indicator is equal to or more than or is equal to or less than the quantile. Doing so would enable a user to give a threshold level of confidence that an impurity is not lower than some ratio, or is not more than some ratio. One may be interested either in estimating either whether an impurity is lower than some standard, or whether an impurity is greater than some standard.
Additionally, it would be reasonable for a user to wish to set a threshold of interest. Therefore, it would have been obvious to one of ordinary skill in the art practicing the invention of Golshan in view of Denny and Yanagisawa and Walpole to cause the threshold to be received by the input receiver.
Regarding claim 5, Golshan in view of Denny and Yanagisawa and Walpole teaches the limitations of claim 2. Furthermore, it would have been obvious to cause the data processing device to further comprise an input receiver that receives a user’s input, to cause the test of claim 4 to be performed, and to cause the threshold to be received by the input receiver, all for the reasons given in the rejection of claim 4 (see rejection of claim 4).
Noting the above, it would have been obvious to one of ordinary skill in the art practicing the invention of Golshan in view of Denny and Yanagisawa and Walpole to cause the display processor to be operable to display a percentile point of the posterior predictive distribution of the quantitative indicator, the percentile point corresponding to the threshold. This claim language is met by displaying the concentration ratio
Q
at which the threshold is reached in the equation
T
=
P
I
≤
Q
=
∫
0
Q
f
x
d
x
, since
Q
gives the value below which a given percentage
T
of all values of the posterior predictive distribution exist (which is a common meaning of “percentile point”; see rejection of claim 2).
Regarding claim 6, Golshan in view of Denny and Yanagisawa and Walpole teaches the limitations of claim 2. Furthermore, it would have been obvious to one of ordinary skill in the art practicing the invention of Golshan in view of Denny and Yanagisawa and Walpole to cause the display processor to be operable to, when the input receiver newly receives an input of the threshold, display the percentile point again while bringing the percentile point into correspondence with the newly received threshold. This just describes inputting a new value, running the test again, and displaying the new result. This is commonly expected functionality and would enable one to perform multiple tests.
Regarding claim 12, claim 12 describes the method applied by the analysis device at claim 2 and is therefore rejected for the same reasons.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN WESLEY EDWARDS whose telephone number is (571)272-0266. The examiner can normally be reached Monday - Friday, 7:30am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ETHAN WESLEY EDWARDS
Examiner
Art Unit 2857
/E.W.E./ Examiner, Art Unit 2857
/ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857