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. 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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites quantifying a component that is included in a treatment liquid obtained by performing a purification treatment on a liquid including a specific protein and impurities other than the protein and acquiring an estimated value of a concentration of the impurities on the basis of spectral data indicating an intensity of electromagnetic waves, which have been emitted to the treatment liquid and have been subjected to an action of the treatment liquid, for each wave number or wavelength, wherein the concentration of the impurities included in the treatment liquid is equal to or less than 20 mg/mL, and a weight ratio of the impurities to a mixture including the protein and the impurities is equal to or less than 15% which falls into the abstract idea grouping of mathematical and mental concepts . The claimed “q uantifying ” and “ acquiring ” an estimate from spectral data is considered by the examiner as mathematical process es that transforms raw physical observations , i.e. electromagnetic waves , into actionable information, integrating structured observation s , evaluation, and judgment. The identified abstract idea functions as a mathematical bridge between raw signals and quantitative observations into the physical properties for estimating a purification state. This judicial exception is not integrated into a practical application because a treatment liquid, a purification treatment, liquid, specific protein and impurities merely read as additional elements that generically link the abstract idea to a field of use; as neither the performance or result of the abstract idea improves or affects these additional elements . The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the identified additional element related to what is being estimated in terms of purification are not impacted by the determination of a concentration. These elements merely link the mathematical and mental process to a field of use without amounting to significantly more . Claim 2 recites quantifying a component that is included in a treatment liquid obtained by performing a purification treatment on a liquid including a specific protein and impurities other than the protein; and acquiring an estimated value of a concentration of an immature sugar chain that has a structure similar to that of the protein on the basis of spectral data indicating an intensity of electromagnetic waves, which have been emitted to the treatment liquid and have been subjected to an action of the treatment liquid, for each wave number or wavelength which falls into the abstract idea grouping of mathematical and mental concepts. The claimed “q uantifying ” and “ acquiring ” an estimate from spectral data is considered by the examiner as a mathematical process that transforms raw physical observations , i.e. electromagnetic waves , into actionable information, integrating structured observation s , evaluation, and judgment. The identified abstract idea functions as a mathematical bridge between raw signals and quantitative observations into the physical properties for estimating a purification state. This judicial exception is not integrated into a practical application because a treatment liquid, a purification treatment, liquid, immature sugar chain, a structure, specific protein and impurities merely read as additional elements that generically link the abstract idea to a field of use; as neither the performance or result of the abstract idea improves or affects these additional elements . The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the identified additional element related to what is being estimated in terms of purification are not impacted by the determination of a concentration. These elements merely link the mathematical and mental process to a field of use without amounting to significantly more . Claim 3 further defines the abstract idea falling into the abstract ideas of both mathematical and mental concepts without integrating the abstract idea into a practical application or providing significantly more; as acquiring an estimated value of a concentration of the protein included in the treatment liquid on the basis of the spectral data is based on mathematical concepts. Claim 4 further defines the additional element protein without integrating the abstract idea into a practical application or providing significantly more; as the cultured cell is neither impacted or improved by the mathematical performance/result and merely links the abstract idea to a field of use. MPEP 2106.05(h) Claim 5 further defines the additional element impurities without integrating the abstract idea into a practical application or providing significantly more; as the DNA of a cell producing the protein, an aggregate of the protein, a decomposition product of the protein, and a host cell protein are neither impacted or improved by the mathematical performance/result and merely links the abstract idea to a field of use. MPEP 2106.05(h) Claim 6 further defines the additional element protein without integrating the abstract idea into a practical application or providing significantly more; as the purification treatment includes a component separation method using chromatography is neither impacted or improved by the mathematical performance/result and merely links the abstract idea to a field of use. MPEP 2106.05(h) Claims 7 and 8 further define the abstract idea falling into the abstract idea grouping of mathematical concepts without integrating the abstract idea into a practical application or provide significantly more. The examiner considers a determination coefficient and a root mean squared error to be part of the mathematical processes for estimating the purification state. Claim 9 further recites an additional element soft sensor. The soft sensor, as disclosed by applicant’s specification, is a collection of models and algorithms receiving the spectral data. Machine learning is considered to use mathematical concepts when training the ML model to performing an operation like estimating the concentration value from the spectral data. Therefore, claim 9 is considered to further define the abstract idea without providing significantly more or integrating the abstract idea into a practical application; as the implied computer environment performing the mathematical operations is merely acting as a tool for performing the abstract idea without providing significantly more or integrating the abstract idea into a practical application. MPEP 2106.05(a) Claims 10-15 further define the abstract idea by reciting preprocessing and selecting the spectral data, which as supported by applicant’s specification involves mathematic operations, i.e. sparse modeling [0021]. Therefore, the claim 10 is considered to further define the abstract idea without providing significantly more or integrating the abstract idea into a practical application. Claim 16 further defines the spectral data collected. The data merely links the abstract idea to a field of use without providing significantly more or integrating the abstract idea into a practical application. MPEP 2106.05(h) Claim 17 further defines the state data includes an estimated value of a concentration of the protein included in the treatment liquid. The further defined state data merely links the abstract idea to a field of use without providing significantly more or integrating the abstract idea into a practical application. MPEP 2106.05(h) Claim Rejections - 35 USC § 102 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale , or otherwise available to the public before the effective filing date of the claimed invention. Claim(s) 1 -17 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Jungbauer et al. (WO 2017/174580A1) . With respect to claim 1, Jungbauer et al. teaches a method for estimating a purified state (as Jungbauer et al. teaches monitoring the purification; [0086]) , the method comprising: quantifying a component (using ATR-FTIR and an ion exchange chromatography unit; [0022] [0042] ) that is included in a treatment liquid (i.e. a biopharmaceutical; [0025]) obtained by performing a purification treatment (as Jungbauer et al. teaches using the disclosed method is used to purify any biopharmaceutical; [0025]) including a specific protein (i.e. an antibody; [0059]) and impurities other than the protein (as Jungbauer et al. teaches the purification process using a combination of chromatography and mam malian treatment to remove viral contaminants; [0004]) ; and acquiring an estimated value of a concentration of the impurities on the basis of spectral data indicating an intensity of electromagnetic waves (as the disclosed ATR-FTIR uses spectral data indicating an intensity of electromagnetic waves; [0014] ) , which have been emitted to the treatment liquid (i.e. the biopharmaceutical; [0025]) and have been subjected to an action of the treatment liquid (for example, the application of electromagnetic waves during ATR-FTIR ) , for each wave number or wavelength (during the ATR-FTIR process) , wherein the concentration of the impurities included in the treatment liquid is equal to or less than 20 mg/mL (as Jungbauer et al. teaches a measurement range of (1.50mg/mL; [00186], which is less than 20mg/mL) , and a weight ratio of the impurities to a mixture including the protein and the impurities is equal to or less than 15% ( as para. [0 0241 ] discloses a mixture of 20ppm, which equates to a weight ratio of 0.002% ) . With respect to claim 2 , Jungbauer et al. teaches a method for estimating a purified state (as Jungbauer et al. teaches monitoring the purification; [0086]) , the method comprising: quantifying a component (using ATR-FTIR and an ion exchange chromatography unit; [0022] [0042]) that is included in a treatment liquid (i.e. a biopharmaceutical; [0025]) obtained by performing a purification treatment (as Jungbauer et al. teaches using the disclosed method is used to purify any biopharmaceutical; [0025]) on a liquid including a specific protein (i.e. an antibody; [0059]) and impurities other than the protein (as Jungbauer et al. teaches the purification process using a combination of chromatography and mammalian treatment to remove viral contaminants; [0004]) ; and acquiring an estimated value of a concentration of an immature sugar chain that has a structure similar to that of the protein on the basis of spectral data indicating an intensity of electromagnetic waves (as [0025] teaches “[t] he methods of the invention may be used for the purification and/or concentration of any biological product, such as a biopharmaceutical e.g. a nucleic acid molecule or a heterologous protein. Preferred proteins are therapeutic proteins, enzymes and peptides, protein antibiotics, fusion proteins, carbohydrate-protein conjugates , structural proteins, regulatory proteins, vaccines and vaccine like proteins or particles, process enzymes, growth factors, hormones and cytokines or antibodies ; the examiner considers the disclosed carbohydrate-protein conjugates to read on the claimed an immature sugar chain ) , which have been emitted to the treatment liquid (via the ATR-FTIR ) and have been subjected to an action liquid (for example, the application of electromagnetic waves during ATR-FTIR ) of the treatment liquid (biopharmaceutical) , for each wave number or wavelength (via the spectral response of the applied electromagnetic energy) . With respect to claim 3, Jungbauer et al. teaches the method further comprising: acquiring an estimated value of a concentration of the protein included in the treatment liquid (biopharmaceutical; [0025]) on the basis of the spectral data (as the disclosed ATR-FTIR produces spectral data used to determine the concentration of the protein includes in the biopharmaceutical using multivariate analysis; [0057] [0076]) . With respect to claim 4, Jungbauer et al. teaches the method wherein the protein is produced from a cultured cell (as Jungbauer et al. teaches using cell cultures in the analysis; [0091] [00216]) . With respect to claim 5, Jungbauer et al. teaches the method wherein the impurities include DNA of a cell producing the protein (i.e. gene; [0059]) , an aggregate of the protein [0057] , a decomposition product of the protein (peptides; [0055]) , and a host cell protein (HCP; [0047]) . With respect to claim 6, Jungbauer et al. teaches the method wherein the purification treatment includes a component separation method using chromatography (as taught in [0076]) . With respect to claim 7, Jungbauer et al. teaches the method wherein a determination coefficient indicating a degree of match of the estimated value of the concentration of the impurities with a measured value is equal to or greater than 0.9 (as Jungbauer teaches in Example 7, page 61, section e), the average prediction error RMSE 0.97 which is higher than 0.9 ) . With respect to claim 8, Jungbauer et al. teaches the method wherein a root mean squared error indicating a degree of deviation of the estimated value of the concentration of the impurities from a measured value is equal to or less than 1.2 (as Jungbauer et al. teaches a prediction model with a taught prediction performance determined for run 3 shown in figure 24 is RMSE = 1.38 ) . With respect to claim 9, Jungbauer et al. teaches the method further comprising: constructing a soft sensor (i.e. a model and software package; [00148]) , which receives the spectral data (from ATR-FTIR /chromatographic; Fig. 5) as an input (seen in Fig. 5) and outputs state data indicating a purified state of the liquid including the protein and the impurities (as the model and software outputs the prediction of concentration, purity, of the biological product; [00157]) , with machine learning using a plurality of combinations of the state data and the spectral data as training data (to preform multivariate data analysis; [0094] ) ; and inputting the spectral data acquired for the treatment liquid (i.e. the biopharmaceutical; [0025]) to the soft sensor (i.e. model and software; Fig. 5) and acquiring the state data output from the soft sensor (model and software) , wherein the state data includes the estimated value of the concentration of the impurities included in the treatment liquid (as the model and software outputs the prediction of concentration, purity, of the biological product; [00157], using inputted spectral data to the soft sensor that sense the data to the machine learning system to estimate the value of concentrations on impurities included in the treatment liquid) . With respect to claim 10, Jungbauer et al. teaches the method further comprising: performing preprocessing on the spectral data (Fig. 5; [00112]) ; and constructing the soft sensor (model and software; [00148]) with machine learning using a plurality of combinations of processed data obtained by the preprocessing and the state data as training data (as Jungbauer et al. teaches in [00112] and Figure 5 and train the model and software with the recited training data set; [00240] ). With respect to claim 11, Jungbauer et al. teaches the method wherein the preprocessing includes a process of selecting, from spectral intensity values for each wave number or wavelength included in the spectral data, a spectral intensity value used as the training data (as Jungbauer et al. teaches using the chromatographic data, which includes by definition, intensity values for each wave number and spectral intensity values; Jungbauer further teaches training the machine learning model using this data; [00112]) . With respect to claim 12, Jungbauer et al. teaches the method wherein, among the spectral intensity values for each wave number or wavelength included in the spectral data, the number of spectral intensity value selected to be used as the training data is equal to or greater than 5 and less than 1000 (as Fig. 15, E which shows the spectral data falling within 5 and less than 1000) . With respect to claim 13, Jungbauer et al. teaches the method wherein the selection is performed by sparse modeling (as Jungbauer et al. teaches Lasso machine learning modeling [00166]; note Lasso modeling is a type of sparse modeling) . With respect to claim 14, Jungbauer et al. teaches the method wherein the preprocessing includes specifying high-correlation spectral data having a relatively high correlation with the state data among the spec tral data as the processed data (as [00236] teaches [s] tructured additive regression models can be seen as powerful extensions of linear models allowing for inclusion of e.g. non-parametric smooth effects or interactions between predictors. Boosting as a variable selection tool constructs the final model in a stepwise process by minimizing a loss-function and preserving the additivity of the model structure and provides a variable importance measure via predictor selection frequencies. Generally, the variable selection step is very critical as it needs to provide a relevant subset of inputs for the real time prediction of the responses (protein concentration, purity and potency). Hence, variables that do not provide additional information for the prediction of the response(s) should not be contained in the final model and therefore be remov ed during variable selection; this teaches a stepwise model, by selecting only the best-fitting base-learner to update the model, iteratively, the algorithm performs automatic variable selection while maintaining the additive model structure; thereby reading on the claimed invention). With respect to claim 15, Jungbauer et al. teaches the method wherein the preprocessing includes a baseline correction of the spectral data (as Jungbauer et al. teaches using baseline correction for spectral data; [00254]). With respect to claim 16, Jungbauer et al. teaches the method wherein the spectral data is data indicating a spectrum of scattered light of light emitted to the liquid including the protein and the impurities (as the disclosed chromatographic process detects the scattered light emitted from the biopharmaceutical liquid including the protein and impurities within; [00112]) . With respect to claim 17, Jungbauer et al. teaches the method wherein the state data includes an estimated value of a concentration of the protein included in the treatment liquid (as Jungbauer et al. teaches state data included in the training data contains c oncentration, purity and potency determined in various different analytical methods ; [00184]) . Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sadowski et al. (2014/0260536) which teaches mass spectrometry to determine experimental chromatographic data in samples. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT MATTHEW G MARINI whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-2676 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 8am-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, FILLIN "SPE Name?" \* MERGEFORMAT Stephen Meier can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-2149 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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