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 .
DETAILED ACTION
This Office Action is sent in response to Application’s Communication received on 02/15/2024 for application number 19/300571. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawing, Abstract, Oath/Declaration, and Claims.
Claims (1-8), 9, 10, (11-17) are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/04/2023 was filed prior to current Office Action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 Dicoi Ovidiu et al. Foreign Application Publication DE 102016013365 A (hereinafter Dicoi).
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 generate a carbohydrate content value providing a quantitative indication of a level of carbohydrate content in the sample.
However in analogous of measuring galactooligosaccharide, Dicoi teaches generate a carbohydrate content value providing a quantitative indication of a level of carbohydrate content in the sample (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).
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 Dicoi by incorporating the method of generate a carbohydrate content value providing a quantitative indication of a level of carbohydrate content in the sample of Dicoi 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 determining the concentration of enzymes and substrates in the reaction mixture. (Dicoi: page. 9 ¶ 2).
Regarding claim 2, Baker as modified by Dicoi teach wherein the sample is a milk- based substrate (page. 19 ¶ 3, page. 20 ¶ 4, page. 21 ¶ 3 wherein Dicoi provides data samples that includes milk-based substrate).
Regarding claim 3, Baker as modified by Dicoi 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 Dicoi 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 Dicoi 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 carbohydrate content in the training sample (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).
Regarding claim 10, Baker as modified by Dicoi 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 (Abstract, page. 10 ¶ 5, wherein Dicoi 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 Dicoi Ovidiu et al. Foreign Application Publication DE 102016013365 A (hereinafter Dicoi) and further in view of Cho Byoung Kwan et al. Foreign Application Publication KR 101608626 B1 (hereinafter Kwan).
Regarding claim 6, Baker and Dicoi 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 Dicoi 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 Dicoi 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 Dicoi Ovidiu et al. Foreign Application Publication DE 102016013365 A (hereinafter Dicoi) 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 Dicoi and Kwan teaches the carbohydrate 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)
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, Dicoi 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, Dicoi 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 Dicoi, 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 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).
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 Dicoi Ovidiu et al. Foreign Application Publication DE 102016013365 A (hereinafter Dicoi).
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 (
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).
Beck does not teach a measured indication of a level of carbohydrate content in the training sample.
However in analogous of measuring galactooligosaccharide, Dicoi teaches a measured indication of a level of carbohydrate content in the training sample (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).
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 Dicoi by incorporating the method of a measured indication of a level of carbohydrate content in the training sample. of Dicoi 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 determining the concentration of enzymes and substrates in the reaction mixture. (Dicoi: page. 9 ¶ 2).
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 Dicoi Ovidiu et al. Foreign Application Publication DE 102016013365 A (hereinafter Dicoi).
Regarding claim 11, Beck teaches A method for preparing a milk product containing carbohydrate, comprising: treating a milk-based substrate with a trans-galactosylating enzyme (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), ([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) and determining, based on the carbohydrate content value, when to inactivate the trans- galactosylating enzyme by pasteurization of the milk base ([0006-0007], [0185] wherein Beck determines the level of carbohydrate in breastmilk and determines measurementof 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)
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 a carbohydrate content value providing a quantitative indication of a level of carbohydrate content in the sample.
However in analogous of measuring galactooligosaccharide, Dicoi teaches a carbohydrate content value providing a quantitative indication of a level of carbohydrate content in the sample (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).
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 Dicoi by incorporating the method of a carbohydrate content value providing a quantitative indication of a level of carbohydrate content in the sample of Dicoi 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 determining the concentration of enzymes and substrates in the reaction mixture. (Dicoi: page. 9 ¶ 2).
Regarding claim 12, Beck as modified by Baker and Dicoi 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 carbohydrate content value, and the carbohydrate 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).
Regarding claim 13, Beck as modified by Baker and Dicoi teach having an accuracy better than 10%, expressed as Standard Error of Prediction at mean value (3.75 %), the concentration of GOS 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),
Claims 14-15 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 Dicoi Ovidiu et al. Foreign Application Publication DE 102016013365 A (hereinafter Dicoi) 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 Dicoi 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 Dicoi do not teach having a linearity (R2) of the PLS regression model above 0.9 to validate the GOS content.
However in analogous of measuring galactooligosaccharide, Kwan teaches having a linearity (R2) of the PLS regression model above 0.9 to validate the GOS 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 Baker and Dicoi by incorporating the method of having a linearity (R2) of the PLS regression model above 0.9 to validate the GOS content 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 Dicoi 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).
Regarding claim 15, Beck as modified by Baker, Dicoi and Kwali teach wherein the carbohydrate is one or more of GOS, DP3+ GOS, glucose, galactose, and DP2, wherein preferably DP2 is lactose and/or the carbohydrate is DP3+ GOS ([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), (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).
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 Dicoi Ovidiu et al. Foreign Application Publication DE 102016013365 A (hereinafter Dicoi) 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, Dicoi 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, Dicoi 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, Dicoi 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, Dicoi, 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/HASSAN MRABI/Examiner, Art Unit 2144
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