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 Amendment
This Office action has been issued in response to amendment filed on 09/10/2026, Claims (1-14, 16-17) are pending. Applicants' arguments have been carefully and respectfully considered and addressed. Accordingly, this action has been made FINAL necessitated by amendment.
Claims (1-14, 16-17) are presented for examination.
Claim Objections
Claims 1, 5, 7-9, 11-14 are objected to because of the following informalities: DP3+ should be defined before it is used as an abbreviation. Appropriate correction is required.
Response to Arguments
Applicants' arguments have been carefully and respectfully considered and addressed. The arguments presented are moot based on amendment.
Regarding claim 13, the Applicant’s argument does not provide evidence whether the claim invokes or doesn’t invoke 35 USC § 112 (f), therefore; the argument is not persuasive.
With regards to Applicant’s argument that pertains to obviousness rejection, the prior arts are all directed to the same invention that include measuring galactooligosaccharides or similar composition. Therefore, Examiner applied Prima Facie of Obviousness with recognized equivalent for the same purpose (2144.06(I)). The following is the examples of rationale (2143(I)):
Examples of rationales that may support a conclusion of obviousness include:[AltContent: rect]
(A) Combining prior art elements according to known methods to yield predictable results; [AltContent: rect]
(B) Simple substitution of one known element for another to obtain predictable results; [AltContent: rect]
(C) Use of known technique to improve similar devices (methods, or products) in the same way; [AltContent: rect]
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results; [AltContent: rect]
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success; [AltContent: rect]
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art; [AltContent: rect]
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
All Applicant’s argument seems to pertain to DP3+ galactooligosaccharide amendment. The arguments were fully considered and are moot in view of the new ground rejection wherein Matthew J Baker. NPL, “Using Fourier transform IR spectroscopy to analyze biological materials” published 2014 (hereinafter Baker) and further in view of Tams Jeppe Wegner. Foreign Application Publication AU 2018269327 B2 (hereinafter Wegner), wherein the combination of the new ground rejection teaches the claims’ limitations based on the amendment.
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 of this title, 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.
Claims 1-5 and 10 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Matthew J Baker. NPL, “Using Fourier transform IR spectroscopy to analyze biological materials” published 2014 (hereinafter Baker) and further in view of Tams Jeppe Wegner. Foreign Application Publication AU 2018269327 B2 (hereinafter Wegner).
Regarding claim 1, Baker teaches A method for determining carbohydrate content in a sample, the method comprising (page. 2, ¶ 4 wherein Baker describes recoding data from a variety of samples, wherein the data includes spectral analyses that delineates cellular hierarchy on the basis of protein, lipid and carbohydrate composition) obtaining FTIR (Fourier Transform Infrared Spectroscopy) spectrum data corresponding to the sample (FIG. 1, page. 1, Abstract, ¶ 1-2, wherein Baker incorporates Fourier Transform Infrared Spectroscopy for spectrum data) providing at least a portion of the FTIR spectrum data as an input to a trained machine learning model (page. 11, ¶ 5-6, page. 17, page. 18, ¶ 2, wherein Baker processes spectrum data through a machine learning model).
Baker teaches and processing at least a portion of the FTIR spectrum data using the trained machine learning mode (FIG. 3, page. 3, ¶ 1, page. 9, ¶ 6, page. 10, ¶ 1 wherein Baker teaches processing samples data using trained machine learning model).
Baker does not teach generating DP3+ galactooligosaccharide content value providing a quantitative indication of a level of DP3+ galactooligosaccharide content in the sample.
However in analogous of measuring galactooligosaccharide, Wegner teaches generating DP3+ galactooligosaccharide content value providing a quantitative indication of a level of DP3+ galactooligosaccharide content in the sample (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, page. 22, wherein Wegner describes a method of a quantitative determination, measurement of direct quantification of GOS and producing DP3+ galactooligosaccharide (GOS) of diary product, wherein the method provides indications of a degree of polymerization and the level of DP3+GOS).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Baker with Wegner by incorporating the method of generating DP3+ galactooligosaccharide content value providing a quantitative indication of a level of DP3+ galactooligosaccharide content in the sample of Wegner into the method of processing at least a portion of the FTIR spectrum data using the trained machine learning mode of Baker for the purpose of incorporating the method of DP3+ galactooligosaccharide measurement of Wegner into the trained machine learning model of Baker and provides a milk product which comprises a total amount of at least 11 wt% of in situ produced DP3+ galactooligosaccharides (GOS) and wherein the in situ produced DP3+ GOS constitute at least 35 wt% of the total free carbohydrates (Wegner: page. 2 ¶ 2).
Regarding claim 2, Baker as modified by Wegner teach wherein the sample is a milk- based substrate (page. 2, Wherein Wegner teaches sample that is milk-based).
Regarding claim 3, Baker as modified by Wegner teach in which the portion of the FTIR spectrum data supplied as the input to the trained machine learning model comprises FTIR spectrum data within a limited spectral range, wherein preferably the limited spectral range includes 1046cm-1, 1076cm-1, 1157 cm-1 and 1250 cm-1 (FIG. 1, Abstract, page. 1 ¶ 1 wherein baker describes FTIR spectrum data as input to a trained learning model, wherein the spectrum data measured based on wavenumber within a range of (600–1,450 cm−1)).
Regarding claim 4, Baker as modified by Wegner teach in which the limited spectral range comprises a wavenumber region for which a lower bound is between 900 cm-1 and 1100 cm-1 and an upper bound is between 1300 cm-1 and 1500 cm-1, wherein preferably the lower bound is between 1008 cm-1 and 1068 cm-1 and the upper bound is between 1414 cm-1 and 1475 cm-1, wherein more preferably the limited spectral range comprises wavenumber region 1037:1450 cm-1. (FIG. 1, Abstract, page. 1 ¶ 1 wherein baker describes FTIR spectrum data as input to a trained learning model, wherein the spectrum data measured based on wavenumber within a range of (600–1,450 cm−1), (1,500–1,700 cm−1) and wavenumber region (2,550–3,500 cm−1).
Regarding claim 5, Baker as modified by Wegner teach in which the trained machine learning model comprises a supervised learning model trained using a training data set comprising, for each of a plurality of training samples, the FTIR spectrum data corresponding to the training sample and a measured indication of the level of DP3+ galactooligosaccharide content in the training sample (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, page. 22, wherein Wegner describes a method of a quantitative determination, measurement of direct quantification of GOS and producing DP3+ galactooligosaccharide (GOS) of diary product, wherein the method provides indications of a degree of polymerization and the level of DP3+GOS), (FIG. 1, Abstract, page. 1 ¶ 1 wherein baker describes FTIR spectrum data as input to a trained learning model, wherein the spectrum data measured based on wavenumber within a range of (600–1,450 cm−1), (1,500–1,700 cm−1) and wavenumber region (2,550–3,500 cm−1).
Regarding claim 10, Baker as modified by Wegner teach A computer program which, when executed on a data processing apparatus, controls the data processing apparatus to perform the method of any of the previous claims (FIG. 3, page 3, wherein Baker describes an apparatus and data processing).
Claims 6 is rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Matthew J Baker. NPL, “Using Fourier transform IR spectroscopy to analyze biological materials” published 2014 (hereinafter Baker) and further in view of Tams Jeppe Wegner. Foreign Application Publication AU 2018269327 B2 (hereinafter Wegner) and further in view of Cho Byoung Kwan et al. Foreign Application Publication KR 101608626 B1 (hereinafter Kwan).
Regarding claim 6, Baker and Wegner do not teach in which the trained machine learning model comprises:(a) a partial least squares regression (PLSR) model; (b) a Neural Network regression model; (c) multiple-linear regression (MLR); (d) principle components regression (PCR); (e) classical least squares method (CLS); or (f) a decision tree algorithm.
However in analogous of measuring galactooligosaccharide, Kwan teaches in which the trained machine learning model comprises:(a) a partial least squares regression (PLSR) model; (b) a Neural Network regression model; (c) multiple-linear regression (MLR); (d) principle components regression (PCR); (e) classical least squares method (CLS); or (f) a decision tree algorithm (Abstract, page. 3, ¶ 8-10 wherein Kwan provides models with Partial least squares regression analysis (PLSR), Principal Component Regression (PCR) and Principal Component Analysis (PCA).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Kwan with Baker and Wegner by incorporating the method of in which the trained machine learning model comprises:(a) a partial least squares regression (PLSR) model; (b) a Neural Network regression model; (c) multiple-linear regression (MLR); (d) principle components regression (PCR); (e) classical least squares method (CLS); or (f) a decision tree algorithm of Kwan into the method of processing at least a portion of the FTIR spectrum data using the trained machine learning mode of Baker and Wegner for the purpose of incorporating a spectroscopic analysis technique that is capable of real-time measurement in a non-destructive manner and has the advantage of simultaneously measuring various components. (Kwan: page. 2 ¶ 12).
Claims 7-8 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Matthew J Baker. NPL, “Using Fourier transform IR spectroscopy to analyze biological materials” published 2014 (hereinafter Baker) and further in view of Tams Jeppe Wegner. Foreign Application Publication AU 2018269327 B2 (hereinafter Wegner) and further in view of Cho Byoung Kwan et al. Foreign Application Publication KR 101608626 B1 (hereinafter Kwan) and further in view of Beck et al. US Patent Application Publication US 20210255110 A1 (hereinafter Beck).
Regarding claim 7, Baker as modified by Wegner and Kwan teaches the DP3+ galactooligosaccharide content value is made accessible to the client device (FIGS. 1-2, page. 2 wherein Baker describes method of providing FTIR image spatial as illustrated in FIG. 1 that shows biomarkers of the carbohydrate values), (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, page. 22, wherein Wegner describes a method of a quantitative determination, measurement of direct quantification of GOS and producing DP3+ galactooligosaccharide (GOS) of diary product, wherein the method provides indications of a degree of polymerization and the level of DP3+GOS).
Baker, Dicoi and Kwan do not teach in which the FTIR spectrum data is obtained from a server-based data store to which the FTIR spectrum data is uploaded by a client device, wherein preferably the carbohydrate content value is made accessible to the client device.
However in analogous of measuring galactooligosaccharide, Beck teaches in in which the FTIR spectrum data is obtained from a server-based data store to which the FTIR spectrum data is uploaded by a client device, wherein preferably the carbohydrate content value is made accessible to the client device (Claim 17 text, [0005], [0051], [0097], [0103], [0185] wherein Beck determines carbohydrate and protein using breastmilk macronutrients concentration test and provides the mother with results regarding the tested breastmilk and with nutritional and practical recommendations. Wherein an accumulation of galactose from lactose breakdown is detected in culture supernatants from L. acidophilus (approximately 3.5 mM) and L. mesenteroides, (lactic acid bacteria fermentation of human breastmilk oligosaccharide components, human breastmilk oligosaccharides and galactooligosaccharides. V. S. O'leary et al., (Applied AND Environmental Microbiology, 1976, pp. 89-94) shows that fermentation of lactose in breastmilk was accompanied by the release of free galactose. Wherein Beck provides a user device a scanning element for scanning testing regions of the test; testing the breastmilk using at least one test strips and scanning said testing regions using said user device; applying machine learning algorithms at an online remote cloud server, wherein the algorithms are designed to incorporate uploaded user's data and data obtained from said user device and provide an output stating whether the breastmilk is spoiled or not and/or has deficiencies in certain nutrients, and further provide an output with recommendations for nutritional regimen to improve breastmilk's quality; and providing an online interface to access said outputs).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Beck with Baker, Wegner and Kwan by incorporating the method of teaches in in which the FTIR spectrum data is obtained from a server-based data store to which the FTIR spectrum data is uploaded by a client device, wherein preferably the carbohydrate content value is made accessible to the client device of Beck into the method of processing at least a portion of the FTIR spectrum data using the trained machine learning mode of Baker, Wegner and Kwan for the purpose of detecting deficiencies in certain nutrients, and producing recommendations for nutritional components, quantities and consumption frequency for overcoming said deficiencies and improving breastmilk's quality (Beck: claim 17 text).
Regarding claim 8, Baker as modified by Wegner, Kwan and Beck teach in which the processing of at least a portion of the FTIR spectrum data using the trained machine learning model is performed at a server device using the FTIR spectrum data obtained from a client device, wherein preferably the DP3+ galactooligosaccharide content value is made accessible to the client device (Claim 17 text, [0005], [0051], [0097], [0103], [0185] wherein Beck determines carbohydrate and protein using breastmilk macronutrients concentration test and provides the mother with results regarding the tested breastmilk and with nutritional and practical recommendations. Wherein an accumulation of galactose from lactose breakdown is detected in culture supernatants from L. acidophilus (approximately 3.5 mM) and L. mesenteroides, (lactic acid bacteria fermentation of human breastmilk oligosaccharide components, human breastmilk oligosaccharides and galactooligosaccharides. V. S. O'leary et al., (Applied AND Environmental Microbiology, 1976, pp. 89-94) shows that fermentation of lactose in breastmilk was accompanied by the release of free galactose. Wherein Beck provides a user device a scanning element for scanning testing regions of the test; testing the breastmilk using at least one test strips and scanning said testing regions using said user device; applying machine learning algorithms at an online remote cloud server, wherein the algorithms are designed to incorporate uploaded user's data and data obtained from said user device and provide an output stating whether the breastmilk is spoiled or not and/or has deficiencies in certain nutrients, and further provide an output with recommendations for nutritional regimen to improve breastmilk's quality; and providing an online interface to access said outputs), (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, page. 22, wherein Wegner describes a method of a quantitative determination, measurement of direct quantification of GOS and producing DP3+ galactooligosaccharide (GOS) of diary product, wherein the method provides indications of a degree of polymerization and the level of DP3+GOS).
Claim 9 is rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Beck et al. US Patent Application Publication US 20210255110 A1 (hereinafter Beck) and further in view of Matthew J Baker. NPL, “Using Fourier transform IR spectroscopy to analyze biological materials” published 2014 (hereinafter Baker) and further in view of Tams Jeppe Wegner. Foreign Application Publication AU 2018269327 B2 (hereinafter Wegner).
Regarding claim 9, Beck teaches A method for training a machine learning model to predict carbohydrate content in a milk-based substrate; the method comprising (Claim 14 text, [0010], [0046], [0050-0051], [0096], [0102-0103], [0192], [0199], [0201], [0205], [0208-0213] wherein Beck incorporates a machine learning model to detect and measure various parameters and components within breastmilk) and performing supervised learning using the training data set (Abstract, wherein Baker performs supervised or unsupervised learning).
Becker does not teach predicting DP3+ galactooligosaccharide content in a milk-based substrate; and a measured indication of a level of DP3+ galactooligosaccharide content in the training sample.
However in analogous of measuring galactooligosaccharide, Wegner teaches predicting DP3+ galactooligosaccharide content in a milk-based substrate (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, pages. 21- 22, wherein Wegner determines DP3+ galactooligosaccharide content in a milk) and a measured indication of a level of DP3+ galactooligosaccharide content in the training sample (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, page. 22, wherein Wegner describes a method of a quantitative determination, measurement of direct quantification of GOS and producing DP3+ galactooligosaccharide (GOS) of diary product, wherein the method provides indications of a degree of polymerization and the level of DP3+GOS).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Baker with Wegner by incorporating the method of generating DP3+ galactooligosaccharide content value providing a quantitative indication of a level of DP3+ galactooligosaccharide content in the sample, and a measured indication of a level of DP3+ galactooligosaccharide content in the training sample of Wegner into the method of processing at least a portion of the FTIR spectrum data using the trained machine learning mode of Baker for the purpose of incorporating the method of DP3+ galactooligosaccharide measurement of Wegner into the trained machine learning model of Baker and provides a milk product which comprises a total amount of at least 11 wt% of in situ produced DP3+ galactooligosaccharides (GOS) and wherein the in situ produced DP3+ GOS constitute at least 35 wt% of the total free carbohydrates (Wegner: page. 2 ¶ 2).
Beck does not teach obtaining a training data set comprising, for each of a plurality of training samples, FTIR (Fourier Transform Infrared Spectroscopy) spectrum data corresponding to the training sample.
However in analogous of measuring galactooligosaccharide, Baker teaches obtaining a training data set comprising, for each of a plurality of training samples, FTIR (Fourier Transform Infrared Spectroscopy) spectrum data corresponding to the training sample (FIG. 3, Abstract, page. 3, ¶ 1, page. 9, ¶ 6, page. 10, ¶ 1 wherein Baker teaches processing samples data using trained machine learning model and data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type) to determine trained model coefficients for the machine learning model (page. 11, wherein Baker describes Clustering (unsupervised classification) wherein the Clustering aims at sort-ing different objects (i.e., spectra) into categories or clusters on the basis of a so-called distance measure. Clustering methods such as hierarchical cluster analysis (HCA) and k-means cluster-ing (KMC) are frequently used in IR-imaging studies to identify tissue morphology23,125. HCA groups spectra into mutually exclu-sive clusters; in IR-imaging studies, HCA-based segmentation is achieved by assigning a distinct color to the spectra in one cluster. Because each spectrum of an IR-imaging experiment has a unique spatial (x,y) position, pseudocolor segmentation maps can be eas-ily generated by plotting specifically colored pixels as a function of the spatial coordinates. Baker also describes Supervised or concept-driven classi-fication techniques are machine-learning techniques for creat-ing a classification function from training data. These methods involve a supervised learning procedure in which models are cre-ated that map input objects (spectra) to desired outputs (class assignments). Popular supervised techniques are artificial neural networks, support vector machines (Supplementary Method 3), linear discriminant classifier11,103,126 and Bayesian inference-base methods77. Among the many criteria guiding the choice of clas-sifier, the most important is probably the accuracy (related to sensitivity and specificity) when tested on an independent test data set. Other criteria include ease to train, computational time, spatial resolution considerations127 and software availability. Baker incorporates FE For diagnosis that constitutes an important data reduction step in order to match the complexity of the subsequent supervised classifier with the amount of data available so as to avoid over-fitting or undertraining. PCA is one particular popular form of unsupervised FE that is used for this purpose103. The number of PCA factors to retain may be subject to optimization. One way out is to order the PCA factors from the most to the least discriminant on the basis of their P values as determined by a statistical test. The percentage of explained variance can also be taken into account. Within FE, the subgroup of feature selection (FS) methods is particularly interesting because it can confer biological inter-pretability (i.e., identify the wavenumbers most important for classification) to the classification system. Popular FS methods include forward FS120 and COVAR121. Variance analyses may also be used to select spectral variables for elimination122. Another approach to FS is to use spectral features that are obtained from a biochemical understanding of the problem123. These cases in which direct spectral interpretation is possible are termed metrics for measures of biochemical activity in the samples. It is impor-tant to note that not all metrics may be useful biomarkers. Thus, even FE may be a multistep process, (i.e., one in which metrics are converted to statistically relevant biomarkers).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Beck with Baker by incorporating the method of obtaining a training data set comprising, for each of a plurality of training samples, FTIR (Fourier Transform Infrared Spectroscopy) spectrum data corresponding to the training sample of Baker into the method of method for training a machine learning model to predict carbohydrate content in a milk-based substrate of Beck for the purpose of data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. (Baker: Abstract).
Claims 11-13 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Beck et al. US Patent Application Publication US 20210255110 A1 (hereinafter Beck) and further in view of Matthew J Baker. NPL, “Using Fourier transform IR spectroscopy to analyze biological materials” published 2014 (hereinafter Baker) and further in view of Tams Jeppe Wegner. Foreign Application Publication AU 2018269327 B2 (hereinafter Wegner).
Regarding claim 11, Beck teaches A method for preparing a milk product containing galactooligosaccharides, comprising: treating a milk-based substrate with a trans-galactosylating enzyme (Claim 17 text, [0005], [0051], [0097], [0103], [0185] wherein Beck determines carbohydrate including galactooligosaccharides ([0005]) and protein using breastmilk macronutrients concentration test and provides the mother with results regarding the tested breastmilk and with nutritional and practical recommendations. Wherein an accumulation of galactose from lactose breakdown is detected in culture supernatants from L. acidophilus (approximately 3.5 mM) and L. mesenteroides, (lactic acid bacteria fermentation of human breastmilk oligosaccharide components, human breastmilk oligosaccharides and galactooligosaccharides. V. S. O'leary et al., (Applied AND Environmental Microbiology, 1976, pp. 89-94) shows that fermentation of lactose in breastmilk was accompanied by the release of free galactose. Wherein Beck provides a user device a scanning element for scanning testing regions of the test; testing the breastmilk using at least one test strips and scanning said testing regions using said user device; applying machine learning algorithms at an online remote cloud server, wherein the algorithms are designed to incorporate uploaded user's data and data obtained from said user device and provide an output stating whether the breastmilk is spoiled or not and/or has deficiencies in certain nutrients, and further provide an output with recommendations for nutritional regimen to improve breastmilk's quality; and providing an online interface to access said outputs), ([0017], [0072], [0092] wherein Beck detects and analyzes umerous cells, such as immune cells, microbiome creating bacteria, stem cells and many more, as well as other factors such as enzymes and antibodies, all of which continue “working” after the breastmilk exits the breast and thus modify the content of the breastmilk during storage).
Beck does not teach performing FTIR (Fourier Transform Infrared Spectroscopy) on a sample of the milk-based substrate to obtain FTIR spectrum data corresponding to the sample.
However in analogous of measuring galactooligosaccharide, Baker teaches performing FTIR (Fourier Transform Infrared Spectroscopy) on a sample of the milk-based substrate to obtain FTIR spectrum data corresponding to the sample (FIG. 3, Abstract, page. 3, ¶ 1, page. 9, ¶ 6, page. 10, ¶ 1 wherein Baker teaches processing samples data using trained machine learning model and data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type) to determine trained model coefficients for the machine learning model (page. 11, wherein Baker describes Clustering (unsupervised classification) wherein the Clustering aims at sort-ing different objects (i.e., spectra) into categories or clusters on the basis of a so-called distance measure. Clustering methods such as hierarchical cluster analysis (HCA) and k-means cluster-ing (KMC) are frequently used in IR-imaging studies to identify tissue morphology23,125. HCA groups spectra into mutually exclu-sive clusters; in IR-imaging studies, HCA-based segmentation is achieved by assigning a distinct color to the spectra in one cluster. Because each spectrum of an IR-imaging experiment has a unique spatial (x,y) position, pseudocolor segmentation maps can be eas-ily generated by plotting specifically colored pixels as a function of the spatial coordinates. Baker also describes Supervised or concept-driven classi-fication techniques are machine-learning techniques for creat-ing a classification function from training data. These methods involve a supervised learning procedure in which models are cre-ated that map input objects (spectra) to desired outputs (class assignments). Popular supervised techniques are artificial neural networks, support vector machines (Supplementary Method 3), linear discriminant classifier11,103,126 and Bayesian inference-base methods77. Among the many criteria guiding the choice of clas-sifier, the most important is probably the accuracy (related to sensitivity and specificity) when tested on an independent test data set. Other criteria include ease to train, computational time, spatial resolution considerations127 and software availability. Baker incorporates FE For diagnosis that constitutes an important data reduction step in order to match the complexity of the subsequent supervised classifier with the amount of data available so as to avoid over-fitting or undertraining. PCA is one particular popular form of unsupervised FE that is used for this purpose103. The number of PCA factors to retain may be subject to optimization. One way out is to order the PCA factors from the most to the least discriminant on the basis of their P values as determined by a statistical test. The percentage of explained variance can also be taken into account. Within FE, the subgroup of feature selection (FS) methods is particularly interesting because it can confer biological inter-pretability (i.e., identify the wavenumbers most important for classification) to the classification system. Popular FS methods include forward FS120 and COVAR121. Variance analyses may also be used to select spectral variables for elimination122. Another approach to FS is to use spectral features that are obtained from a biochemical understanding of the problem123. These cases in which direct spectral interpretation is possible are termed metrics for measures of biochemical activity in the samples. It is impor-tant to note that not all metrics may be useful biomarkers. Thus, even FE may be a multistep process, (i.e., one in which metrics are converted to statistically relevant biomarkers).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Beck with Baker by incorporating the method of performing FTIR (Fourier Transform Infrared Spectroscopy) on a sample of the milk-based substrate to obtain FTIR spectrum data corresponding to the sample of Baker into the method of method for training a machine learning model to predict carbohydrate content in a milk-based substrate of Beck for the purpose of data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. (Baker: Abstract).
Baker teaches obtaining, based on processing of at least a portion of the FTIR spectrum data using a trained machine learning model (FIG. 3, page. 3, ¶ 1, page. 9, ¶ 6, page. 10, ¶ 1 wherein Baker teaches processing samples data using trained machine learning model).
Baker does not teach obtaining DP3+ galactooligosaccharide content value providing a quantitative indication of a level of DP3+ galactooligosaccharide content in the sample;
However in analogous of measuring galactooligosaccharide, Wegner teaches obtaining DP3+ galactooligosaccharide content value providing a quantitative indication of a level of DP3+ galactooligosaccharide content in the sample (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, pages. 21- 22, wherein Wegner determines DP3+ galactooligosaccharide content in a milk), (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, page. 22, wherein Wegner describes a method of a quantitative determination, measurement of direct quantification of GOS and producing DP3+ galactooligosaccharide (GOS) of diary product, wherein the method provides indications of a degree of polymerization and the level of DP3+GOS).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Baker with Wegner by incorporating the method of obtaining DP3+ galactooligosaccharide content value providing a quantitative indication of a level of DP3+ galactooligosaccharide content in the sample of Wegner into the method of processing at least a portion of the FTIR spectrum data using the trained machine learning mode of Baker for the purpose of incorporating the method of DP3+ galactooligosaccharide measurement of Wegner into the trained machine learning model of Baker and provides a milk product which comprises a total amount of at least 11 wt% of in situ produced DP3+ galactooligosaccharides (GOS) and wherein the in situ produced DP3+ GOS constitute at least 35 wt% of the total free carbohydrates (Wegner: page. 2 ¶ 2).
Beck as modified by Baker and Wegner teaches determining, based on the DP3+ galactooligosaccharide content value, when to inactivate the trans- galactosylating enzyme by pasteurization of the milk base ([0005-0007], [0185] wherein Beck determines the level of carbohydrate in breastmilk and determines measurement of dornic acidity that could be considered a simple and economical method to select breastmilk to pasteurize in a human breastmilk bank based on quality and safety criteria).
Regarding claim 12, Beck as modified by Baker and Wegner teach in which at least a portion of the FTIR spectrum data is uploaded by a client device to a server for server-based processing by the trained machine learning model to generate the DP3+ galactooligosaccharide content value, and the DP3+ galactooligosaccharide content value is obtained by the client device as a result of the server-based processing (Claim 17 text, [0005], [0051], [0097], [0103], [0185] wherein Beck determines carbohydrate and protein using breastmilk macronutrients concentration test and provides the mother with results regarding the tested breastmilk and with nutritional and practical recommendations. Wherein an accumulation of galactose from lactose breakdown is detected in culture supernatants from L. acidophilus (approximately 3.5 mM) and L. mesenteroides, (lactic acid bacteria fermentation of human breastmilk oligosaccharide components, human breastmilk oligosaccharides and galactooligosaccharides. V. S. O'leary et al., (Applied AND Environmental Microbiology, 1976, pp. 89-94) shows that fermentation of lactose in breastmilk was accompanied by the release of free galactose. Wherein Beck provides a user device a scanning element for scanning testing regions of the test; testing the breastmilk using at least one test strips and scanning said testing regions using said user device; applying machine learning algorithms at an online remote cloud server, wherein the algorithms are designed to incorporate uploaded user's data and data obtained from said user device and provide an output stating whether the breastmilk is spoiled or not and/or has deficiencies in certain nutrients, and further provide an output with recommendations for nutritional regimen to improve breastmilk's quality; and providing an online interface to access said outputs), (page. 3 ¶ 3, page. 8 ¶ 8, page. 9 ¶ 2, page. 20 ¶ 5, page. 21 ¶ 8-11 wherein Dicoi describes incorporating FTIR spectroscopy for measuring hydrolysis levels of the carbohydrates and performing analysis of the enzymes according to standard methods for the identification of the proteins and determination of the enzyme activity that are for the qualitative and quantitative determination of amylase / cellulase coulometric methods and the release of the reduced sugars or the cleavage products of chromogenic substrates. Wherein the Carbohydrates with a high content of polysaccharides and a low content of oligomers provide low DE values. High DE values show the cleavage products with shorter chain length or molecular mass. The theoretical DE values can be calculated from the ratio of the molecular weight of the glucose, based on the molecular weight of the saccharides multiplied by 100), (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, page. 22, wherein Wegner describes a method of a quantitative determination, measurement of direct quantification of GOS and producing DP3+ galactooligosaccharide (GOS) of diary product, wherein the method provides indications of a degree of polymerization and the level of DP3+GOS).
Regarding claim 13, Beck as modified by Baker and Wegner teach having an accuracy better than 10%, expressed as Standard Error of Prediction at mean value (3.75 %), the concentration of DP3+ galactooligosaccharide in a concentration range of 0-7.5 % in a milk base containing at least 0.1% fat, at least 0.5% dissolved lactose, and at least 1% protein ([0015], [0056], [0072], [0147], [0171] wherein Beck measures the range and concentration of lactose, protein and fat and prepares solution by dissolving b-galactosidase (20 U), glucose oxidase (20 U) and horseradish peroxidase (200 U), (Abstract, claims 1, 8 text, page. 2, ¶ 8-9, page. 3, page. 5, page 6, ¶ 5-6, page. 9, ¶ 4, page. 10, ¶ 3-7, page. 22, wherein Wegner describes a method of a quantitative determination, measurement of direct quantification of GOS and producing DP3+ galactooligosaccharide (GOS) of diary product, wherein the method provides indications of a degree of polymerization and the level of DP3+GOS), (page. 2, ¶ 7-10 wherein Wegner describes DP3+ galactooligosaccharide in mild concentration).
Claims 14 is rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Beck et al. US Patent Application Publication US 20210255110 A1 (hereinafter Beck) and further in view of Matthew J Baker. NPL, “Using Fourier transform IR spectroscopy to analyze biological materials” published 2014 (hereinafter Baker) and further in view of Tams Jeppe Wegner. Foreign Application Publication AU 2018269327 B2 (hereinafter Wegner) and further in view of Cho Byoung Kwan et al. Foreign Application Publication KR 101608626 B1 (hereinafter Kwan).
Regarding claim 14, Beck as modified by Baker and Wegner teach GOS content (Claim 17 text, [0005], [0051], [0097], [0103], [0185] wherein Beck determines carbohydrate and protein using breastmilk macronutrients concentration test and provides the mother with results regarding the tested breastmilk and with nutritional and practical recommendations. Wherein an accumulation of galactose from lactose breakdown is detected in culture supernatants from L. acidophilus (approximately 3.5 mM) and L. mesenteroides, (lactic acid bacteria fermentation of human breastmilk oligosaccharide components, human breastmilk oligosaccharides and galactooligosaccharides. V. S. O'leary et al., (Applied AND Environmental Microbiology, 1976, pp. 89-94) shows that fermentation of lactose in breastmilk was accompanied by the release of free galactose. Wherein Beck provides a user device a scanning element for scanning testing regions of the test; testing the breastmilk using at least one test strips and scanning said testing regions using said user device; applying machine learning algorithms at an online remote cloud server, wherein the algorithms are designed to incorporate uploaded user's data and data obtained from said user device and provide an output stating whether the breastmilk is spoiled or not and/or has deficiencies in certain nutrients, and further provide an output with recommendations for nutritional regimen to improve breastmilk's quality; and providing an online interface to access said outputs), (page. 3 ¶ 3, page. 8 ¶ 8, page. 9 ¶ 2, page. 20 ¶ 5, page. 21 ¶ 8-11 wherein Dicoi describes incorporating FTIR spectroscopy for measuring hydrolysis levels of the carbohydrates and performing analysis of the enzymes according to standard methods for the identification of the proteins and determination of the enzyme activity that are for the qualitative and quantitative determination of amylase / cellulase coulometric methods and the release of the reduced sugars or the cleavage products of chromogenic substrates. Wherein the Carbohydrates with a high content of polysaccharides and a low content of oligomers provide low DE values. High DE values show the cleavage products with shorter chain length or molecular mass. The theoretical DE values can be calculated from the ratio of the molecular weight of the glucose, based on the molecular weight of the saccharides multiplied by 100).
Beck as modified by Baker and Wegner do not teach having a linearity (R2) of the PLS regression model above 0.9 to validate the DP3+ galactooligosaccharide content.
However in analogous of measuring galactooligosaccharide, Kwan teaches having a linearity (R2) of the PLS regression model above 0.9 to validate the DP3+ galactooligosaccharide content (Abstract, page. 3, ¶ 8-10, PAGE. 8 ¶ 9-10 wherein Kwan provides models with Partial least squares regression analysis (PLSR), Principal Component Regression (PCR) and Principal Component Analysis (PCA). Kwan test samples as R2).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Kwan with Beck, Baker and Wegner by incorporating the method of having a linearity (R2) of the PLS regression model above 0.9 to validate the DP3+ galactooligosaccharide content of Kwan into the method of processing at least a portion of the FTIR spectrum data using the trained machine learning mode of Beck, Baker and Wegner for the purpose of incorporating a spectroscopic analysis technique that is capable of real-time measurement in a non-destructive manner and has the advantage of simultaneously measuring various components. (Kwan: page. 2 ¶ 12).
Claims 16-17 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Beck et al. US Patent Application Publication US 20210255110 A1 (hereinafter Beck) and further in view of Matthew J Baker. NPL, “Using Fourier transform IR spectroscopy to analyze biological materials” published 2014 (hereinafter Baker) and further in view of Tams Jeppe Wegner. Foreign Application Publication AU 2018269327 B2 (hereinafter Wegner) and further in view of Cho Byoung Kwan et al. Foreign Application Publication KR 101608626 B1 (hereinafter Kwan) and further in view of Larsen et al. US 20190021352 A1 (hereinafter Larsen).
Regarding claim 16, Beck, Baker, Wegner and Kwan do not teach wherein the trans- galactosylating enzyme is derived from Bifidobacterium bifidum, wherein preferably the enzyme is a truncated P-galactosidase from Bifidobacterium bifidum, most preferably truncated on the C-terminus.
However in analogous of measuring galactooligosaccharide, Larsen teaches wherein the trans- galactosylating enzyme is derived from Bifidobacterium bifidum, wherein preferably the enzyme is a truncated P-galactosidase from Bifidobacterium bifidum, most preferably truncated on the C-terminus ([0003-0031], [0127], [0142], [0238], [0247-0249], [0275], [0329], [0367], [0379], [0396], [0417] wherein Larsen teaches a truncation variant (OLGA347) as being a transgalactosylating enzyme from Bifidobacterium bifidum).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Larsen with Beck, Baker, Wegner and Kwan and Kwan by incorporating the method of having a linearity (R2) of the PLS regression model above 0.9 to validate the GOS content of Larsen into the method of processing at least a portion of the FTIR spectrum data using the trained machine learning mode of Beck, Baker, Wegner and Kwan for the purpose of providing an enzyme composition comprising a polypeptide which has transgalactosylating activity, but which enzyme composition has no or substantially no activity attributable to the following enzymes: cellulose, mannanase, pectinase, and optionally amylase. (Larsen: [0019]).
Regarding claim 17, Beck as modified by Baker, Wegner, Kwan and Larsen teach wherein the truncated (3-galactosidase from Bifidobacterium bifidum comprises a polypeptide having at least 70%, at least 80%, at least 90%, at least 95% or at least 99% sequence identity to SEQ ID. NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4 or SEQ ID NO:5 or to a transgalactosylase active fragment thereof, wherein preferably the polypeptide comprises a sequence according to SEQ ID ([0003-0031], [0127], [0142], [0238], [0247-0249], [0275], [0329], [0367], [0379], [0396], [0417] wherein Larsen teaches polypeptide having at least 70%, at least 80%, at least 90%, at least 95% or at least 99%).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/HASSAN MRABI/Examiner, Art Unit 2144
