CTNF 18/286,702 CTNF 88017 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 9 and 19 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. 07-34-03 AIA The term “ substantially ” in claim s 9 and 19 is a relative term which renders the claim indefinite. The term “ substantially ” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA 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”) 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 removing a first set of outliers from the first data set using a univariate statistical technique (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) 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 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) . 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 6, Jiang discloses the method of claim 1, wherein the first multivariate model is a partial least squares model ([0090-0091]) . Regarding claim 7, Jiang 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 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 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 . 07-21-aia AIA Claim (s) 4-5, 8, 10-11, 15-16, 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20060161403 A1-Jiang et al (Hereinafter referred to as “Jiang”), in view of US 20080183101 A1-Stonehouse et al (Hereinafter referred toa s “Stonehouse”) . Regarding claim 4, Jiang discloses the method of claim 1 (See claim 1) , Jiang fails to disclose wherein removing the second set of outliers includes generating Hotelling's T² statistics and removing observations based on the Hotelling's T² statistics. However, in the same field of endeavor, Stonehouse discloses 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) . 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 wherein removing the second set of outliers includes generating Hotelling's T² statistics and removing observations based on the Hotelling's T² statistics as taught by Stonehouse, to improve statistical data ([0113], Stonehouse). Regarding claim 5, Jiang discloses the method of claim 1 (See claim 1) , Jiang fails to disclose wherein removing the second set of outliers includes calculating DModX values and removing observations based on the DModX values. However, in the same field of endeavor, 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) . 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 wherein removing the second set of outliers includes calculating DModX values and removing observations based on the DModX values as taught by Stonehouse, to improve statistical data ([0113], Stonehouse). Regarding claim 8, Jiang discloses the method of claim 1 (See claim 1) , Jiang fails to disclose wherein obtaining the first data set includes accessing a database storing historical data. However, in the same field of endeavor, Stonehouse discloses wherein obtaining the first data set includes accessing a database storing historical data ([0039]) . 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 wherein obtaining the first data set includes accessing a database storing historical data as taught by Stonehouse, to improve statistical data ([0113], Stonehouse). Regarding claim 10, Jiang discloses the method of claim 1 (See claim 1) , Jiang fails to disclose inferring a value or classification using the second multivariate model. However, in the same field of endeavor, Stonehouse discloses 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). . 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 inferring a value or classification using the second multivariate model as taught by Stonehouse, to improve statistical data ([0113], Stonehouse). Regarding claim 11, analyses are analogous to those presented for claim 10 and are applicable for claim 11. 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 20, analyses are analogous to those presented for claim 10 and are applicable for claim 20 . 07-21-aia AIA 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”), 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 substantially in real-time 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 Any inquiry concerning this communication or earlier communications from the examiner should be directed to LERON BECK whose telephone number is (571)270-1175. The examiner can normally be reached M-F 8 am-5pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Czekaj can be reached at (571) 272-7327. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. LERON . BECK Examiner Art Unit 2487 /LERON BECK/Primary Examiner, Art Unit 2487 Application/Control Number: 18/286,702 Page 2 Art Unit: 2487 Application/Control Number: 18/286,702 Page 4 Art Unit: 2487 Application/Control Number: 18/286,702 Page 5 Art Unit: 2487 Application/Control Number: 18/286,702 Page 6 Art Unit: 2487 Application/Control Number: 18/286,702 Page 7 Art Unit: 2487 Application/Control Number: 18/286,702 Page 8 Art Unit: 2487 Application/Control Number: 18/286,702 Page 9 Art Unit: 2487 Application/Control Number: 18/286,702 Page 10 Art Unit: 2487
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