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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. GB2015861.4, filed on 10/07/2020.
Claim Status
Claims 1-24 are currently pending and under examination herein.
Claim(s) 1-24 is are/rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/05/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. A signed copy of a list of references cited from each IDS is included in this Office Action.
Drawings
The drawings filed on 4/05/2023 are accepted.
Specification
The specification filed on 4/05/2023 is accepted.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim
limitation(s) uses a generic placeholder that is coupled with functional language without
reciting sufficient structure to perform the recited function and the generic placeholder is
not preceded by a structural modifier. Such claim limitation(s) is/are:
“A variable measurement module configured to…” in claim 13
“A data transformation module configured to…” in claim 13
“A data training module configured to…” in claim 13
“A performance predicting module configured to…” in claim 13
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
In cases involving a special purpose computer-implemented means-plus-function
limitation, the Federal Circuit has consistently required that the structure be more than
simply a general-purpose computer or microprocessor and that the specification must
disclose an algorithm for performing the claimed function. See, e.g., Noah Systems Inc.
V. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239.
Regarding claim 13, there is no disclosure that links to any particular structure in the specification, and therefore the Examiner interprets each of the “variable measurement module” and “performance predicting module” as any means to achieve the corresponding function with respect to the prior art search. With respect to the “data transformation module” and “data training module”, there is sufficient description and these terms are interpreted as a computer-implemented system that transforms data (see paragraph [0033] and paragraphs [0043]-[0045] in applicant’s specification) and a computer-implemented system that trains a time series analysis model (see paragraph [0035] and paragraphs [0043]-[0045] in applicant’s specification), respectively. Similarly, the limitations reciting “a performance predicting module” is also interpreted as a computer-implemented system that is based on the time-series analysis model above.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 13-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a written description rejection.
Although the specification provides a description of what each module is configured to do
throughout, it does not provide a sufficient written description of the structure for each of the
corresponding modules. The specification mentions broadly, for example, " the variable measurement module 104 measures each parameter at a predetermined sampling frequency for a plurality of batches of the bioreactor” (page 10), however, there is not sufficient detail as to how the measurement is done. Furthermore, there is no mention of whether the module is comprised of a generic computer processor or linked to any particular structure. The lack of sufficient structure description applies to the variable measurement module. Appropriate correction is required.
Original claims may lack written description when the claims define the invention in functional
language specifying a desired result but the specification does not sufficiently describe how the function
is performed or the result is achieved. Generic claim language in the original disclosure does not satisfy
the written description requirement if it fails to support the scope of the genus claimed (See MPEP
2106.01 and 2163).
Claims 13-23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites the following structures: the variable measurement module. It is unclear whether these modules correspond to particular structures configured to perform the recited functions or merely represent generic computer-implemented functional elements. Accordingly, the metes and bounds of the claim cannot be determined with reasonable certainty, and the claim therefore is indefinite under 35 U.S.C. 112(b). Appropriate correction is required.
Claim 21 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 21 recites the limitation " top, middle or bottom up mass analysis " in line 1. The claims recite “top … bottom up mass analysis”, however, these terms have been introduced without prior recitation in claim 20, and therefore are not further limiting. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-24 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, Prong 1).
Claim 1 and 13 recites sequentially joining the parameter data of the n number of runs in a single continuous time-series manner, to generate a transformed data that indicates parameter data over a time duration of the n number of runs; training a time series analysis model for forecasting performance of the biopharmaceutical manufacturing process, based on a plurality of transformed data generated corresponding to a plurality of parameters; using said measurement of the plurality of parameters at a predetermined timepoint to reinforce and improve the trained time series analysis model; and predicting the plurality of parameters for a future run of the biopharmaceutical manufacturing process, based on the trained time series analysis model.
Claim 2 and 14 recites the parameter data from an end of a run is joined to parameter data at start of next run.
Claim 4 and 16 recites the time series analysis model is a multivariate time series analysis model.
Claim 5 and 17 recites the multivariate time series analysis model is a vector error correction model.
Claim 6 and 18 recites the time series analysis model is a univariate time series analysis model.
Claim 8 and 19 recites the plurality of parameters includes a plurality of critical process parameters that comprises viable cell density, osmolality, qualitative analysis of at least one metabolite, and quantitative analysis of at least one metabolite.
Claim 9 and 20 recites a plurality of parameters includes a plurality of critical quality attributes that comprises titre, aggregation profile, charge variant analysis, hydrophobic interactions, hydrophilic interactions, and middle-up mass analysis.
Claim 10 recites the middle up mass analysis comprises glycan analysis.
Claim 11 and 22 recites the method as claimed in claim 1, wherein biopharmaceutical is a monoclonal antibody.
Claim 14 recites the parameter data from an end of a run is joined to parameter data at start of next run.
Claim 21 recites the top, middle or bottom-up mass analysis also comprises glycan analysis.
Claim 24 recites sequentially merging values of each of the plurality of critical quality attributes for each unit operation, sequentially merging values of each of the plurality of critical process parameters for each unit operation, e) training a time series analysis model by inputting the merged values in step (b) and step (c), and the measured values in step (d); and f) predicting values of each of the plurality of critical quality attributes and each of the plurality of critical process parameters, for each unit operation, by applying the trained time series analysis model.
The limitations of training a time series analysis model for forecasting performance of the biopharmaceutical manufacturing process, based on a plurality of transformed data generated corresponding to a plurality of parameters, using the measured plurality of parameters to reinforce and improve the trained time series analysis model, predicting the plurality of parameters for a future run of the biopharmaceutical manufacturing process, based on the trained time series analysis model, training a time series analysis model by inputting the merged values in step (b) and step (c), and the measured values in step (d); and f) predicting values of each of the plurality of critical quality attributes and each of the plurality of critical process parameters, for each unit operation, by applying the trained time series analysis model. is a verbal equivalent of a mathematical calculation, and therefore calls under the “mathematical concept grouping of ideas.” Of note, under the broadest reasonable interpretation, training a model using data (both measured and transformed) still encompasses using math itself using comparison for predictive output.
The limitations of sequentially joining the parameter data of the n number of runs in a single continuous time-series manner, to generate a transformed data that indicates parameter data over a time duration of the n number of runs and sequentially merging values of each of the plurality of critical quality attributes for each unit operation, sequentially merging values of each of the plurality of critical process parameters for each unit operation, and the parameter data from an end of a run is joined to parameter data at start of next run, can be practically performed in the human mind and falls under the “mental processes” grouping of ideas. Sequentially joining the parameter data to obtain a transformed data set and joining parameter data from an end of a run to the parameter data at the start of next run, although time-consuming, can be performed using pen and paper. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 and Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016)
The limitations of the time series analysis model is a multivariate time series analysis model, the multivariate time series analysis model is a vector error correction model, the time series analysis model is a univariate time series analysis model, plurality of parameters includes a plurality of critical process parameters that comprises viable cell density, osmolality, qualitative analysis of at least one metabolite, and quantitative analysis of at least one metabolite, a plurality of parameters includes a plurality of critical quality attributes that comprises titre, aggregation profile, charge variant analysis, hydrophobic interactions, hydrophilic interactions, and middle-up mass analysis, middle up mass analysis comprises glycan analysis, the biopharmaceutical manufacturing process is a cell culture process, the biopharmaceutical being a monoclonal antibody, and the top, middle or bottom-up mass analysis also comprises glycan analysis are merely furthering limiting the judicial exception.
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claim 13 recites a computer-implemented system for predicting performance of a biopharmaceutical manufacturing process including a variable measurement module, a data transformation module, a data training module, and a performance predicting module (as interpreted under 112(f)).
Claim 1 and 13 recites measuring a parameter of the biopharmaceutical manufacturing process at a predetermined sampling frequency, wherein said parameter is measured for n number of runs of said process.
Claim 3 and 15 recites the plurality of parameters includes a plurality of critical process parameters (CPPs) and a plurality of critical quality attributes (CQAs).
Claim 7 recites the transformed data enable capturing a seasonality aspect of the parameter data over the time duration of the n number of runs.
Claim 12 and 23 recites the biopharmaceutical manufacturing process is a cell culture process.
Claim 24 recites measuring a plurality of critical quality attributes and a plurality of critical process parameters at a predetermined sampling frequency, for at least one unit operation and predicting values of each of the plurality of critical quality attributes and each of the plurality of critical process parameters, for each unit operation, by applying the trained time series analysis model.
The limitations of measuring a parameter at a predetermined sampling frequency including a variable measurement module, the parameter data from an end of a run is joined to parameter data at start of next run, measuring a plurality of critical quality attributes and a plurality of critical process parameters at a predetermined sampling frequency, for at least one unit operation and predicting values of each of the plurality of critical quality attributes and each of the plurality of critical process parameters, for each unit operation, by applying the trained time series analysis model equates to mere data gathering and processing. Of note, the courts have ruled in Electric Power Group, LLC V. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) that the collection, analysis, and display of data are considered insignificant extra-solution activity and does not integrate the judicial exception into a practical application (see MPEP 2106.05(g)).
Of note, there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The limitations of the plurality of parameters includes a plurality of critical process parameters (CPPs) and a plurality of critical quality attributes (CQAs), the transformed data enable capturing a seasonality aspect of the parameter data over the time duration of the n number of runs, merely further limit the insignificant extra-solution activity of data gathering and does not integrate the judicial exception into a practical application. As such claims 1-24 are directed to an abstract idea (Step 2A Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic field-of-use and/or technological environment. The instant claims recite the following additional elements:
Claim 13 recites a computer-implemented system for predicting performance of a biopharmaceutical manufacturing process including a variable-measurement module, a data transformation module, a data training module, and a performance predicting module (as interpreted under 112(f)).
Claim 1 and 13 recites measuring a parameter of the biopharmaceutical manufacturing process at a predetermined sampling frequency, wherein said parameter is measured for n number of runs of said process.
Claim 3 and 15 recites the plurality of parameters includes a plurality of critical process parameters (CPPs) and a plurality of critical quality attributes (CQAs).
Claim 7 recites the transformed data enable capturing a seasonality aspect of the parameter data over the time duration of the n number of runs.
Claim 12 and 23 recites the biopharmaceutical manufacturing process is a cell culture process.
Claim 24 recites measuring a plurality of critical quality attributes and a plurality of critical process parameters at a predetermined sampling frequency, for at least one unit operation and predicting values of each of the plurality of critical quality attributes and each of the plurality of critical process parameters, for each unit operation, by applying the trained time series analysis model.
As aforementioned, there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems (see claim 13). As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The additional elements recited amount to well-understood, routine, conventional activity. Particularly, the limitations for measuring a parameter of biological manufacturing, such as a cell culture process, wherein the plurality of parameters includes a plurality of critical process parameters (CPPs) and a plurality of critical quality attributes (CQAs) is well-known in scientific literature as evidenced by Campolongo (page 6; application of PAT into biopharmaceutical manufacturing processes using performance indicators). In addition, evaluation of quality parameters in recent literature is also disclosed in the applicant’s specification (page 1-2). Furthermore, the limitations of the transformed data enables capturing a seasonality aspect of the parameter data over the time duration of the n number of runs comprise electronic recordkeeping which the courts have determined to be well-understood, conventional, and routine activity. (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log)).
There are no additional elements that comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-21 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 24 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Downey et al. (WO2019100040A1) as filed in the IDS on 4/05/2023.
Regarding claim 24, Downey teaches:
A method for predicting performance of a biopharmaceutical manufacturing process (see Abstract) the method comprising the steps of:
a) measuring a plurality of critical quality attributes operation (lactate concentration measured in paragraph 32, 33, and 78) and a plurality of critical process parameters (parameters can be pH, dissolved oxygen, glucose concentration; see paragraphs 47, 48, 78, and 84) at a predetermined sampling frequency, for at least one unit (paragraphs 42, 46-48, 51 and 52);
b) sequentially merging values of each of the plurality of critical quality attributes for each unit operation (paragraphs 49 and 54-76; see Fig.3 for past sampling instants);
c) sequentially merging values of each of the plurality of critical process parameters for each unit operation (paragraphs 49 and 54-76; see “three days” section in paragraph 78);
d) measuring the plurality of critical quality attributes and the plurality of critical process parameters at a predetermined timepoint (paragraphs 49 and 54-76);
e) training a time series analysis model by inputting the merged values in step (b) and step (c), and the measured values in step (d) (paragraphs 49 and 54-76); and
f) predicting values of each of the plurality of critical quality attributes and each of the plurality of critical process parameters, for each unit operation, by applying the trained time series analysis model (paragraphs 49 and 54-76; see “future sampling” instants in Fig. 3).
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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
The present rejection(s) reference specific passages from cited prior art. However,
Applicant is advised that the rejections are based on the entirety of each cited prior art. That is,
each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI))
Therefore, Applicant is advised to review all portions of the cited prior art if traversing a
rejection based on the cited prior art.
Claim(s) 1-5, 7-8, 11-17, 19, and 22-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCready (US20140136146 A1) in view of Swaminathan et al. (WO2018229802) as filed in the IDS on 4/05/2023.
Regarding claim 1, McCready teaches:
A method for predicting performance of a biopharmaceutical manufacturing process (predicting prospective behavior of a manufacturing process described in paragraph [0009]), the method comprising the steps of:
measuring a parameter of the biopharmaceutical manufacturing process at a predetermined sampling frequency, wherein said parameter is measured for n number of runs of said process (measured values from historical batch runs and current batch runs in [0009]-[0010], data is collected from multiple historical batch runs as described in [0054]);
sequentially joining the parameter data of the n number of runs in a single continuous time-series manner, to generate a transformed data that indicates parameter data over a time duration of the n number of runs (concatenation described in paragraph [0052]);
training a time series analysis model for forecasting performance of the biopharmaceutical manufacturing process, based on a plurality of transformed data generated corresponding to a plurality of parameters (paragraph [0054]);
measuring the plurality of parameters at a predetermined timepoint (model is complemented with the measured values of the current run in paragraph [0081]);
and predicting the plurality of parameters for a future run of the biopharmaceutical manufacturing process, based on the trained time series analysis model (description of the estimation of a future value described in [0009], specific algorithm disclosed in paragraph [0061]). McCready does not explicitly teach using said measurement to reinforce and improve the trained time series analysis model. However, in the same field of endeavor, Swaminathan teaches predictive modeling using error-based forecast correction (page 14, lines 11-25, explicit residual learning and feedback algorithm described on page 17). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Swaminathan’s iteration into McCready’s predictive biopharmaceutical manufacturing pipeline in order to create a system which continuously learns from new data to predict output features as correctly as possible as disclosed by Swaminathan (page 17). The combination would be accomplished with reasonable expectation of success as both inventions are directed to the same problem in the same field of endeavor.
Regarding claim 2, McCready teaches:
The method as claimed in claim 1, wherein the parameter data from an end of a run is joined to parameter data at start of next run (see paragraphs [0052]-[0056] for details on the multi-batch matrix construction).
Regarding claim 3, McCready teaches:
The method as claimed in claim 1, wherein the plurality of parameters includes a plurality of critical process parameters (CPPs) and a plurality of critical quality attributes (CQAs) (critical process parameters identified in paragraph [0006], critical quality profiles described in paragraph [0016]).
.
Regarding claim 4, McCready teaches:
The method as claimed in claim 1, wherein the time series analysis model is a multivariate time series analysis model (explicit recitation of a multivariate method to estimate future behavior in paragraph [0053]).
Regarding claim 5, Swaminathan teaches:
The method as claimed in claim 4, wherein the multivariate time series analysis model is a vector error correction model (base predictive model iteratively updates using forecast errors calculated as the difference between predicted and actual process values; see page 17). The Examiner notes that the subsequent step involves feeding the errors back into the model for future predictions using metabolite covariates which can be implemented as a layered-error correction framework for multivariate bioprocess forecasting.
Regarding claim 7, McCready teaches:
The method as claimed in claim 1, wherein the transformed data enable capturing a seasonality aspect of the parameter data over the time duration of the n number of runs (see paragraph [0054]; batches are combined into a single X-matrix which would provide patterns of trajectory across runs). The Examiner notes that there is no definition provided for the “seasonality aspect” in the applicant’s specification. Therefore, the Examiner interprets seasonality to mean the inherent structure seen in repeated batch runs.
Regarding claim 8, McCready teaches:
The method as claimed in claim 1, wherein the plurality of parameters includes a plurality of critical process parameters that comprises viable cell density, osmolality, qualitative analysis of at least one metabolite, and quantitative analysis of at least one metabolite (parameter types explicitly described in paragraphs [0016] and paragraph [0079]).
Regarding claim 11, McCready teaches:
The method as claimed in claim 1, wherein biopharmaceutical is a monoclonal antibody (applications of the method apply to monoclonal antibodies as seen in paragraph [0079]).
Regarding claim 12, McCready teaches:
The method as claimed in claim 1, wherein the biopharmaceutical manufacturing process is a cell culture process (explicitly described in paragraph [0079]).
Regarding claim 13, see claim 1.
Regarding claim 14, McCready teaches:
The system as claimed in claim 13, wherein the parameter data from an end of a run is joined to parameter data at start of next run (see paragraphs [0052]-[0056] for details on the multi-batch matrix construction)..
Regarding claim 15, McCready teaches:
The system as claimed in claim 13, wherein the plurality of parameters includes a plurality of critical process parameters (CPPs) and a plurality of critical quality attributes (CQAs) (critical process parameters identified in paragraph [0006], critical quality profiles described in paragraph [0016]).
Regarding claim 16, McCready teaches:
The system as claimed in claim 13, wherein the time series analysis model is a multivariate time series analysis model (explicit recitation of a multivariate method to estimate future behavior in paragraph [0053]).
Regarding claim 17, Swaminathan teaches:
The system as claimed in claim 16, wherein the multivariate time series analysis model is a vector error correction model (base predictive model iteratively updates using forecast errors calculated as the difference between predicted and actual process values; see page 17). The Examiner notes that the subsequent step involves feeding the errors back into the model for future predictions using metabolite covariates which can be implemented as a layered-error correction framework for multivariate bioprocess forecasting.
Regarding claim 19, McCready teaches:
The system as claimed in claim 13, wherein the plurality of parameters includes a plurality of critical process parameters that comprises viable cell density, osmolality, qualitative analysis of at least one metabolite, and quantitative analysis of at least one metabolite (parameter types explicitly described in paragraphs [0016] and paragraph [0079]).
Regarding claim 22, McCready teaches:
The system as claimed in claim 13, wherein the biopharmaceutical is a monoclonal antibody (applications of the method apply to monoclonal antibodies as seen in paragraph [0079]).
Regarding claim 23, McCready teaches:
The system as claimed in claim 13, wherein the biopharmaceutical manufacturing process is a cell culture process (explicitly described in paragraph [0079]).
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCready (US20140136146 A1), as applied in claim 1, in view of Swaminathan et al. (WO2018229802) as filed in the IDS on 4/05/2023, in further view of Jenzch et al (New Bioprocessing Strategies: Development and Manufacturing of Recombinant Antibodies and Proteins. Advances in Biochemical Engineering/Biotechnology, vol 165. Springer, Cham.).
Regarding claim 6,
McCready and Swaminathan teach the claimed invention substantially as stated above. They do not teach the method as claimed in claim 1, wherein the time series analysis model is a univariate time series analysis model. Jenzch teaches the method as claimed in claim 1, wherein the time series analysis model is a univariate time series analysis model (explicitly taught that univariate process parameter analysis is well-understood in the art and a standard method of analysis in the biotechnology industry; page 229.) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute Jenzch’s univariate process parameter analysis tool into McCready’s predictive performance framework in order to obtain a predictably less complex alternative to the multivariate model. The substitution would have been accomplished with a reasonable expectation of success as a univariate model is a less complex method of analysis that is well-known in the art for predictive modeling in biotechnology.
Regarding claim 18, Jenzch teaches:
The system as claimed in claim 18, wherein the time series analysis model is a univariate time series analysis model (see rejection on Claim 6 above).
Claim(s) 9-10 and 20-21 and is/are rejected under 35 U.S.C. 103 as being unpatentable over McCready (US20140136146 A1), as applied in claim 1, in view of Swaminathan et al. (WO2018229802) in further view of Hoehse et al. (EP3702439A1) as filed in the IDS on 4/05/2023.
Regarding claim 9,
McCready and Swaminathan teach the claimed invention substantially as stated above. They do not teach the method as claimed in claim 1, wherein a plurality of parameters includes a plurality of critical quality attributes that comprises titre, aggregation profile, charge variant analysis, hydrophobic interactions, hydrophilic interactions, and middle-up mass analysis. Hoehse teaches the method as claimed in claim 1, wherein a plurality of parameters includes a plurality of critical quality attributes that comprises titre, aggregation profile, charge variant analysis, hydrophobic interactions, hydrophilic interactions, and middle-up mass analysis (Examples of critical quality attributes seen throughout; see paragraph [0090] for aggregation level, paragraph [0126) for titer and glycan profile; mass spectrometry workflow explicitly stated in claim 10). The Examiner notes that the hydrophobic and hydrophilic interactions are also disclosed through the inherent physicochemical make-up of protein composition, charge distribution, glycosylation, and aggregation state which are all explicitly disclosed. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hoehse’s critical quality attributes into McCready’s predictive performance framework in order to include more success criteria to confirm whether a better product was created essentially creating a greater breadth of criteria as disclosed by Hoehse (paragraph [0124]). The combination of features would have had a reasonable expectation of success as both are in the same field of endeavor of predicting the performance of a multivariate process for the composition of a biopharmaceutical product.
Regarding claim 10, Hoehse teaches:
The method as claimed in claim 9, wherein middle up mass analysis comprises glycan analysis (paragraph [0126) for glycan profile).
Regarding claim 20, Hoehse teaches:
The system as claimed in claim 13, wherein the plurality of parameters includes a plurality of critical quality attributes that comprises titre, aggregation profile, charge variant analysis, hydrophobic interactions, hydrophilic interactions, and middle-up mass analysis (Examples of critical quality attributes seen throughout; see paragraph [0090] for aggregation level, paragraph [0126) for titer and glycan profile; mass spectrometry workflow explicitly stated in claim 10). The Examiner notes that the hydrophobic and hydrophilic interactions are also disclosed through the inherent physicochemical make-up of protein composition, charge distribution, glycosylation, and aggregation state which are all explicitly disclosed.
Regarding claim 21, Hoehse teaches:
The system as claimed in claim 20, wherein top, middle or bottom-up mass analysis also comprises glycan analysis (paragraph [0126) for glycan profile).
Conclusion
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/P.N./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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