DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 2,8 and 17 are objected to because of the following informalities:
Claim 2, “the query sample” lacks proper antecedent basis;
Claim 8, “the downsampled second spectral data” lacks proper antecedent basis; and
Claim 17, “the spectroscopy system” lacks proper antecedent basis.
Appropriate corrections are required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 and 10-17 are rejected under 35 U.S.C. 103 as being unpatentable over Tulsyan, U.S. Patent Application Publication No. 2022/0128474 A1 (hereinafter: ‘474) in view of XU et al., U.S. Patent Application Publication No. US 2020/0372225 A1 (hereinafter: ‘225).
As per claim 1, ‘474 discloses a computer-implemented method for monitoring and/or controlling biopharmaceutical process (e.g., See ‘474; [0091], which discloses a computer analyzing a biopharmaceutical process to monitor or control it), the method comprising:
querying, by one or more processors and based on a first spectral scan vector of the biopharmaceutical process obtained by a spectroscopy system, an observation database comprising a plurality of observation data sets associated with past scans of biopharmaceutical processes, wherein each of the observation data sets includes spectral data and a corresponding actual analytical measurement (e.g., See ‘474; [0091] - [0093] and [0098], which disclose a computer using a new spectroscopy scan vector from the bioprocess to search a database of older scans, where each record includes the scan data and the matching actual analytical measurement);
calibrating, by the one or more processors and using the selected training data, a local model specific to the biopharmaceutical process, the local model being trained to predict analytical measurements based on spectral data inputs (e.g., See ‘474; [0095], which discloses using training data to train a model to predict analytical measurements from spectral data inputs); and
predicting, by the one or more processors, an analytical measurement of the biopharmaceutical process, wherein predicting the analytical measurement of the biopharmaceutical process includes using the local model to analyze spectral data that the spectroscopy system generated when scanning the biopharmaceutical process (e.g., See ‘474; [0096], which disclose using the trained model on scan data to predict the current analytical measurement).
However, ‘474 does not adequately disclose that querying the observation database includes determining first parameters defining a set of distributions for the first spectral scan vector, or selecting as training data, from among the plurality of observation data sets, particular observation data sets based on (i) the first parameters and (ii) other parameters defining respective sets of distributions for the plurality of observation data sets.
Although ‘474 selects training data by comparing the query scan vector to stored scan vectors using a distance metric (e.g., Euclidian distance) (e.g., See ‘474; [0094]), ‘474 does not disclose selecting training data based on distribution parameters for the query scan and the stored scans.
‘225 discloses using a VAE that outputs distribution parameters (e.g., mean vector and a covariance matrix) that define a Gaussian distribution for an input, and further discloses using KL divergence with those distribution parameters to compare their differences (e.g., See ‘225; [0051], [0052] and [0084]).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate the teachings of ‘225 into ‘474 for the purpose of more accurately picking the best matching past scans for training using probability distributions, thereby improving prediction accuracy for the current bioprocess.
As per claim 2, ‘474 in view of ‘225 further discloses that the first parameters include processing the query sample using an encoder of a variational autoencoder, and wherein the encoder outputs the first parameters (e.g., See ‘225; [0046], which discloses using the VAE encoder to process the query input and output the distribution parameters (mean and variance)).
As per claim 3, ‘474 in view of ‘225 further discloses that the encoder includes exactly one hidden layer (e.g., See ‘225; [0094], which discloses that the encoder is a single layer).
As per claim 4, ‘474 in view of ‘225 further discloses determining, by the one or more processors, the other parameters using the encoder of the variational autoencoder, wherein the encoder outputs the other parameters (e.g., See ‘225; [0052], which disclose running each stored training sample through the VAE to output its Gaussian distribution parameters (mean vector and covariance matrix)).
As per claim 5, ‘474 in view of ‘225 further discloses selecting the particular observation data sets includes calculating multivariate KL divergence metrics based on the first parameters and the other parameters (e.g., See ‘474; [0094], which discloses selecting training data by calculating a distance metric (Euclidian distance) between the query scan vector and stored scan vectors; Also See ‘225; [0051], [0084[ and [0087], which disclose Gaussian distribution parameters and calculating KL divergence using those parameters).
As per claim 6, ‘474 in view of ‘225 further discloses that calibrating the local model specific to the biopharmaceutical process includes: calibrating a Gaussian process machine learning model specific to the biopharmaceutical process (e.g., See ‘474; [0095], which discloses updating a Gaussian model with the selected training data so that it can predict the lab measurements from spectroscopy scan data).
As per claim 10, ‘474 in view of ‘225 further discloses that using the local model to analyze the spectral data includes using the local model to analyze the first spectral scan vector (e.g., See ‘474; [0096], which disclose using the trained model to analyze the same scan vector used for the query to predict the analytical measurement).
As per claim 11, ‘474 in view of ‘225 further discloses that the predicted analytical measurement of the biopharmaceutical process is a metabolite concentration (e.g., See ‘474; [0004] and [0093], which disclose the system predicting metabolite levels (e.g., glucose or lactate) from the spectroscopy scan).
As per claim 12, ‘474 in view of ‘225 further discloses that the predicted analytical measurement of the biopharmaceutical process is osmolality, viability, viable cell density, or titer (e.g., See ‘474; [0072] and [0090], which disclose the system predicting values like osmolality, viable cell density, and titer from the scan).
As per claim 13, ‘474 in view of ‘225 further discloses that the spectroscopy system is a Raman spectroscopy system (e.g., See ‘474; [0034], which disclose a Raman analyzer and a Raman probe being used to scan the bioprocess).
As per claim 14, ‘474 in view of ‘225 further discloses controlling, by the one or more processors and based on the predicted analytical measurement of the biopharmaceutical process, at least one parameter of the biopharmaceutical process (e.g., See ‘474; [0096] and [0097], which disclose using the predicted analytical measurement to control a bioprocess parameter (glucose concentration)).
As per claim 15, ‘474 in view of ‘225 further discloses causing, by the one or more processors, a user interface to display the predicted analytical measurement (e.g., See ‘474; [0030] and [0048], which disclose showing the system having a display to show information (predicted measurements) to a user).
As per claim 16, the rationale as set forth with respect to the rejection of claim 1, from above, is applied herein. Further, ‘474 in view of ‘225 adequately disclose a spectroscopy system having a probe, which is coupled to one or more processors (e.g., See ‘474; [0025], [0026] and [0028]).
As per claim 17, the rationale as set forth with respect to the rejection of claim 1, from above, is applied herein. It is noted that claim 17 is computer executable instructions which are stored on a non-transitory computer readable storage medium that implement the features of claim 1, and clearly ‘474 in view of ‘225 discloses these features (e.g., See ‘474; [0031] and ‘225; [0142] and [0143]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Tulsyan, U.S. Patent Application Publication No. 2022/0128474 A1 (hereinafter: ‘474) in view of XU et al., U.S. Patent Application Publication No. US 2020/0372225 A1 (hereinafter: ‘225), in further view of Lang et al., U.S. Patent Application Publication No. 2013/0295596 A1 (hereinafter: ‘596).
As per claim 7, ‘474 in view of ‘225 does not specifically disclose that querying the observation database includes downsampling the first spectral scan vector; and using the local model to analyze the spectral data includes downsampling the spectral data.
‘596 discloses these features (e.g., See ‘596; [0074] and [0160], which disclose resampling spectral profiles to reduce the # of data points).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate the teachings of ‘596 into ‘474 in view of ‘225 for the purpose of reducing scan size to fewer data points, which makes the database matching and model analysis run faster using less computer processing and memory.
As per claim 8, ‘474 in view of ‘225, in further view of ‘596 further discloses that querying the observation database includes baseline-correcting the downsampled first spectral scan vector; and using the local model to analyze the spectral data includes baseline-correcting the downsampled second spectral data (e.g., See ‘596; [0162] and [0163], which disclose that after down sampling, the unwanted background baseline is removed so that the comparisons are better).
As per claim 9, ‘474 in view of ‘225, in further view of ‘596 further discloses that querying the observation database includes normalizing the downsampled and baseline-corrected first spectral scan vector; and using the local model to analyze the spectral data includes normalizing the downsampled and baseline-corrected spectral data (e.g., See ‘596; [0164] and [0165], which disclose that after baseline removal, the scans are normalized to consider variation).
References Considered but Not Relied Upon
The following references have been considered but were not relied upon with respect to any prior art rejections:
(1) US 2019/0112569 A1, which discloses using in-situ Raman plus chemometric models to measure nutrients in real time and automatically adjust feeds to keep cell culture conditions on target;
(2) US 2019/0137338 A1, which discloses building a broad Raman calibration models for cell culture, predicting glucose, lactate, and cell density so manufacturing runs can be monitored and controlled;
(3) US 10,563,163 B2, which discloses creating Raman multivariate models that work across reactor scales, letting small scale data predict glucose and other culture parameters in large bioreactors;
(4) US 2022/0018782 A1, which discloses using Raman spectra plus lab test results to train models that estimate viral titre during bioreactor runs, with optional baseline correction and normalization; and
(5) US 2015/0247210 A1, which discloses using near infrared spectroscopy to continuously measure chemicals and cells in bioprocess fluids in real time, enabling automated monitoring and feedback control.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONALD D HARTMAN JR whose telephone number is (571)272-3684. The examiner can normally be reached M-F 8:30 - 4:30 EST.
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/RONALD D HARTMAN JR/Primary Patent Examiner, Art Unit 2119 March 6, 2026
/RDH/