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 Claims
. This is a final action on the merits in response to the reply received 7/23/2026.
Response to Arguments
Applicant’s arguments have been considered but are not persuasive.
Applicant admits that Jiang discloses using multivariate analyses and principle component analysis (PCA) to select variables but does not disclose these techniques to be used on outlier removal. The examiner respectfully disagree. [0028] discloses Univariate analysis is used to filter out those variables that have weak predictive value or correlation with the target variable. The weak values can be interpreted as outliers. In addition, Stonehouse describes that PCA is used identify outliers followed by removal of those outliers. Stonehouse states that PCA is performed on the NMR spectra to identify outliers and that samples having Dmodx greater than 3 standard deviations or abnormally high Hotelling’s T are eliminated in [0111]. Therefore, a POSITA, would have had reason to incorporate Stonehouse’s multivariate outlier detection procedure into Jiang’s automated data analysis process to detect anomalous observations based on relationships among multiple variables rather than relying Soley on individual variable analyses as discloses in Stonehouse, [0113]). Rejection is maintained.
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) 1-3, 6-7, 12-14, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20060161403 A1-Jiang et al (Hereinafter referred to as “Jiang”), US 20080183101 A1-Stonehouse et al (Hereinafter referred toa s “Stonehouse”).
Regarding claim 1, Jiang discloses a method for improving multivariate model performance ([0028]), the method comprising:
obtaining, by one or more processors, a first data set comprising (i) values of a plurality of features and (ii) corresponding labels (Fig. 1 shows data sets with labels);
generating, by the one or more processors, a second data set from the first data set, at least by generating an intermediate data set by detecting a first set of outliers within the first data set and automatically removing the first set of otliers from the first data set with a univariate statistical technique ([0020], identifying and extracting useful features from large data sets; Fig. 4, perform univariate analyses; [0046], wherein outlier data is detected, [0059-62, wherein deleting corresponding record; [0071], wherein univariate analysis is performed on each of the variables in the data set in order to filter out variables that have low correlation with the target variable; [0024], automatically distinguishes between continuous and categorical variables; automatically handles problem data, such as missing values and outliers; automatically partitions the data into random test and train subsets, to protect against sample bias in the data; automatically examines the relationship between each potential variable to find the most promising predictor variables; automatically uses these variables to build an optimal statistical model for a given target variable; and automatically evaluates the accuracy of the models it creates; [0041], wherein may be utilized to automatically analyze any type of data or variables)
generating a first multivariate model using the intermediate data set ([[0072], wherein variables that survive the first filtration stage are standardized. The standardized variables are the intermediate data), and
detecting a second set of outliers within the first data set and automatically removing a second set of outliers from the first data set using the first multivariate model and a multivariate statistical technique ([0084], wherein the normalized variables that survive the preceding filtration steps are combined into a data matrix and then Principal Components Analysis (PCA) is performed on this matrix. PCA is a type of multivariate statistical model; [0085], wherein a final filter is applied to remove outliers); and
generating, by the one or more processors, a second multivariate model using the second data set ([0089], wherein after PCA is completed, the invention is ready to build a model using the retained components in the data set. The examiner notes that the reference uses a univariate analysis as described in the earlier limitations, a multivariate analysis as described in the above limitation and another multivariate analysis as described by the PCA. Therefore, you have a univariate, a first multivariate, and a second multivariate).
In addition, Stonehouse teaches that for subsequnent data set, PCA can be used to identify outliers and that multivariate statistical measures, including DModx and Hotelling T are used to identify and eliminate those outliers ([0111]).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method disclosed by Jiang to disclose detecting a second set of outliers within the first data set and automatically removing a second set of outliers from the first data set using the first multivariate model and a multivariate statistical technique); and generating, by the one or more processors, a second multivariate model using the second data set as taught by Stonehouse to detect anomalous observations based on relationships among multiple variables rather than relying Soley on individual variable analyses as discloses (Stonehouse, [0113]).
Regarding claim 2, Jiang discloses the method of claim 1, wherein removing the first set of outliers includes, for each feature of the plurality of features, removing observations corresponding to values outside a predetermined percentile range ([0159]).
Regarding claim 3, Jiang discloses the method of claim 2, wherein the predetermined percentile range is an interquartile range ([0159]).
Regarding claim 4, Stonehouse discloses the method of claim 1, wherein removing the second set of outliers includes generating Hotelling's T² statistics and removing observations based on the Hotelling's T² statistics ([0075], wherein The identification of sample outliers is a combination of using statistical tools ("distance to model", "Hoteling's T2") and user judgement in terms of rationalising what signals, and hence what reason exists, for the anomalous behaviour. Any outliers that can justifiably be removed from the dataset are removed and the analysis repeated).
Regarding claim 5, Jiang discloses the method of claim 1, Stonehouse discloses wherein removing the second set of outliers includes calculating DModX values and removing observations based on the DModX values ([0075], wherein The identification of sample outliers is a combination of using statistical tools ("distance to model", "Hoteling's T2") and user judgement in terms of rationalising what signals, and hence what reason exists, for the anomalous behaviour. Any outliers that can justifiably be removed from the dataset are removed and the analysis repeated; Principal components analysis (PCA) was then run on all of the NMR data to find outliers (centred scaling applied to all bins). Samples which were significantly over 3 standard deviations in the DModX or were abnormally high on the Hotelling's T.sup.2 were removed as were those with levels of ethanol (shown by the methyl group at 1.2 ppm) significantly above reference phase levels).
Regarding claim 6, Jiang discloses the method of claim 1, wherein the first multivariate model is a partial least squares model ([0090-0091]).
Regarding claim 7, Stonehouse discloses the method of claim 6, wherein the second multivariate model is an updated version of the partial least squares model ([0090-0091]).
Regarding claim 8, Stonehouse discloses the method of claim 1, wherein obtaining the first data set includes accessing a database storing historical data ([0039]).
Regarding claim 10, Stonehouse discloses the method of claim 1, inferring a value or classification using the second multivariate model ([0078], wherein the subsequent PLS-DA analysis ensures the latent variables making up the principal components are such that the PCs focus on class discrimination (e.g. before/after product treatment). In this way, PLS-DA separates classes of samples on the basis of their X-variables (points in the NMR spectra)..
Regarding claim 11, analyses are analogous to those presented for claim 10 and are applicable for claim 11.
Regarding claim 12, analyses are analogous to those presented for claim 1 and are applicable for claim 12.
Regarding claim 13, analyses are analogous to those presented for claim 2 and are applicable for claim 13.
Regarding claim 14, analyses are analogous to those presented for claim 3 and are applicable for claim 14.
Regarding claim 15, analyses are analogous to those presented for claim 4 and are applicable for claim 15.
Regarding claim 16, analyses are analogous to those presented for claim 5 and are applicable for claim 16.
Regarding claim 17, analyses are analogous to those presented for claim 6 and are applicable for claim 17.
Regarding claim 18, analyses are analogous to those presented for claim 7 and are applicable for claim 18.
Regarding claim 20, analyses are analogous to those presented for claim 10 and are applicable for claim 20.
Claim(s) 9 and 19 rejected under 35 U.S.C. 103 as being unpatentable over US 20060161403 A1-Jiang et al (Hereinafter referred to as “Jiang”), US 20080183101 A1-Stonehouse et al (Hereinafter referred toa s “Stonehouse”), in view of US 20030109951 A1-Hsiung et al (Hereinafter referred to as “Hsiung”).
Regarding claim 9, Jiang discloses the method of claim 1 (see claim 1),
Jiang fails to disclose monitoring a process using the second multivariate model.
However, in the same field of endeavor, Hsiung discloses monitoring a process substantially in real-time using the second multivariate model ([0205], performs univariate, multivariate, and SCREAM analyses; 3. allows process models to be built and saved including an interface to equation based, physical model builders; software that monitors real-time sensor data; 4. allows data mining of historical and real-time data).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method disclosed by Jiang to disclose monitoring a process substantially in real-time using the second multivariate model as taught by Hsiung, to improve monitoring and controlling process ([0006], Hsiung).
Regarding claim 19, analyses are analogous to those presented for claim 9 and are applicable for claim 19.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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LERON . BECK
Examiner
Art Unit 2487
/LERON BECK/Primary Examiner, Art Unit 2487
