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 Status
Claims 1-20 are currently pending and under examination herein.
Claims 1-20 are rejected.
Claims 9 and 12 are objected to.
Priority
Priority is acknowledged to PCT/EP2021/050743 filed 01/14/2021 and U.S. application 16796340 field on 02/20/2020. In this action, claims 1-20 are examined as though they had an effective filing date of 02/20/2020. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/19/2022, 12/23/2022a, and 12/23/2022b were in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on 08/19/2022 are accepted.
Claim Objections
Claim 9 and 12 are objected to because of the following informalities:
Claim 9 says the method of any claim 1. This may be a typographical error incurred while amending. This objection can be overcome by deleting the word “any”.
Claim 12 includes the variable mi which is not defined in the claim. Other variables included in the claim (e.g., i and k) are defined, but not m. This objection can be overcome by defining the variable m in the claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claim 2, 4-5, 9, and 11-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
The terms “preferably” (in claims 5 and 16) and “normally” (in claims 2, 15, and 17) are relative terms which renders the cited claims indefinite. The terms “preferably” and “normally” are not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Claims 18-20 depend on Claim 15 and are rendered indefinite due to said dependance.
The phrases "for example", “e.g.”, or “such as” renders claim 4 indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For the purpose of examination, the examples that followed these phrases were considered not required by the claim.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claims 9, 11, 13, 15, and 18-19 recites broad limitations, and the claims also recites “optional” limitations which are the narrower statement of the range/limitation. The claims 9, 11, 13, 15, and 18-19 are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language (“optionally”) is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims. Claims 12, 14, 16-17, and 20 depend on the cited claims and are rendered indefinite due to said dependance. For the purpose of examination, the limitations that followed “optionally” were considered not required by the claim.
Claim 12 is also indefinite because it references equations (4, 4a-f, and 28), which are not presently described in the claims. Each equation must be fully included within the claim, otherwise the meets and bounds of the claim are indefinite. This rejection can be overcome by adding the equations to the claims.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea 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 (Step 2A, Prong 1). Claims 1-14 are directed to a method and claims 15-20 are directed to a system. In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites the limitation - determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of said one or more process conditions for the bioprocess at the one or more maturities. Based on the broadest reasonable interpretation, determining transport rates could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 1 also recites predicting one or more features of the bioprocess based at least in part on the determined specific transport rates. Based on the broadest reasonable interpretation, predicting a feature of a bioprocess could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claims 2-14 depend on claim 1, and thus contain the above issues due to said dependence.
Claim 2 recites the limitation - comparing the specific transport rates or values derived therefrom to one or more predetermined values; and determining on the basis of the comparison whether the process is operating normally. Based on the broadest reasonable interpretation, comparing transport rates and determining the process operation could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 3 recites the limitation - the specific transport rate of a metabolite is the net amount of the metabolite transported between the cells and the culture medium, per cell and per unit of maturity. Based on the broadest reasonable interpretation, specifying the determination could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 6 recites the limitation - predicting one or more features of the bioprocess comprising predicting the value of one or more critical quality attributes (CQAs) of the bioprocess using a predictive model that has been trained to predict CQAs using a set of predictor variables comprising the one or more specific transport rates. Based on the broadest reasonable interpretation, predicting the values could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 7 recites the limitation - the machine learning model is a regression model, or wherein the machine learning model is selected from a linear regression model, a random forest regressor; and or wherein the machine learning model comprises a plurality of machine learning models, wherein each machine learning model has been trained to predict the specific transport rates of an individually selected subset of the one or more metabolites. Based on the broadest reasonable interpretation, implementing these machine learning models could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 8 recites the limitation - the machine learning model has been trained to jointly predict the specific transport rates of the one or more metabolites at a later maturity based at least in part on the values of one or more process conditions for the bioprocess at one or more preceding maturities. Based on the broadest reasonable interpretation, implementing this machine learning model could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 9 recites the limitation - the machine learning model has been trained to predict the specific transport rates of the one or more metabolites at a latest of the plurality of maturities or a later maturity based at least in part on the values of one or more process conditions for the bioprocess at the plurality of maturities, optionally wherein the machine learning model has been trained to predict the specific transport rates of the one or more metabolites at a latest of two distinct maturities or a later maturity based at least in part on the values of one or more process conditions for the bioprocess at the two distinct maturities. Based on the broadest reasonable interpretation, implementing these machine learning models could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 11 recites the limitation - predicting one or more features of the bioprocess comprises determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, optionally wherein determining the concentration of the corresponding one or more metabolites at the later maturity comprises solving respective material balance equations. Based on the broadest reasonable interpretation, determining the values could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claims 12-14 depends on claim 11, and thus contain the above issues due to said dependence.
Claim 12 recites the limitation - determining the concentration of a metabolite i at maturity k, where k is the maturity associated with the predicted specific transport rates, comprises integrating any of equations (4), (4a)-(4f) and (28) between a preceding maturity at which mi is known and maturity k. Based on the broadest reasonable interpretation, determining concentrations could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 13 recites the limitation - determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, and using one of more of said concentrations to determine the value of a biomass related metric at the later maturity, optionally wherein determining the value of a biomass related metric at the later maturity comprises solving a kinetic growth model, optionally further comprising using one or more of the said metabolite concentrations and/or biomass related metric values as inputs to the machine learning model to predict specific transport rates at a further maturity. Based on the broadest reasonable interpretation, determining the values could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 14 depends on claim 13, and thus contain the above issues due to said dependence.
Claim 14 recites the limitation - predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity. Based on the broadest reasonable interpretation, predicting the effects could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 15 recites the limitation - determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of said one or more process conditions for the bioprocess at the one or more maturities. Based on the broadest reasonable interpretation, determining the transport rates could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 15 also recites predicting one or more features of the bioprocess based at least in part on the determined specific transport rates. Based on the broadest reasonable interpretation this prediction could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claims 16-20 depends on claim 15, and thus contain the above issues due to said dependence.
Claim 17 recites the limitation - comparing the specific transport rates or values derived therefrom to one or more predetermined values and determining on the basis of the comparison whether the process is operating normally. Based on the broadest reasonable interpretation making this comparison could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 17 also recites predicting the value of one or more critical quality attributes (CQAs) of the bioprocess using a predictive model that has been trained to predict CQAs using a set of predictor variables comprising the one or more specific transport rates. Based on the broadest reasonable interpretation, predicating the values could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 18 recites the limitation - determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, optionally wherein determining the concentration of the corresponding one or more metabolites at the later maturity comprises solving respective material balance equations. Based on the broadest reasonable interpretation, determining the values could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claims 19-20 depend on claim 18, and thus contain the above issues due to said dependence.
Claim 19 recites the limitation - determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, and using one of more of said concentrations to determine the value of a biomass related metric at the later maturity, optionally wherein determining the value of a biomass related metric at the later maturity comprises solving a kinetic growth model, optionally further comprising using one or more of the said metabolite concentrations and/or biomass related metric values as inputs to the machine learning model to predict specific transport rates at a further maturity. Based on the broadest reasonable interpretation, determining the values could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 20 depends on claim 19, and thus contain the above issues due to said dependence.
Claim 20 recites the limitation - predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity. Based on the broadest reasonable interpretation, predicting the effect of a particular value could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
These limitations recite concepts of determining, comparing, and predicting information that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
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). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 1 recites a method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising obtaining values of one or more process conditions including one or more process parameters, one or more metabolite concentrations and/or one or more biomass-related metrics for the bioprocess at one or more maturities.
Claim 4 recites the one or more process conditions include one or more process parameters selected from dissolved oxygen, dissolved C02, pH, temperature, osmolality, agitation speed, agitation power, headspace gas composition (such as e.g. CO2 pressure), flow rates (such as feed rate, bleed rate, harvest rate), feed medium composition and volume of the culture; wherein the one or more process conditions include one or more biomass related metrics selected from viable cell density, total cell density, cell viability, dead cell density, and lysed cell density; and/or wherein the one or more metabolite concentrations include the concentration of one or more metabolites in the cellular compartment, in the culture medium compartment, or in the cell culture as a whole.
Claim 5 recites values of one or more process conditions used to predict the specific transport rates of the one or more metabolites include at least one metabolite concentration value at the one or more maturities, preferably wherein the values of one or more process conditions further includes at least the value(s) of a further process condition at the one or more maturities, preferably at least the value(s) of two further process conditions at the one or more maturities, and/or wherein the one or more values of metabolite concentrations include the concentration of one or more metabolites for which specific transport rates are determined.
Claim 7 recites the use of an artificial neural network (ANN), and a combination thereof.
Claim 9 recites obtaining values of one or more process conditions at one or more maturities comprises obtaining values of the one or more process conditions at a plurality of maturities.
Claim 10 recites the values of one or more process conditions used as input to the machine learning model are associated with a plurality of maturities that are separated from each other by a difference in maturity that is approximately equal to the difference in maturity between the values used to train the machine learning model.
Claim 15 recites a system for monitoring and/or controlling a bioprocess, the system including: at least one processor; and at least one non-transitory computer readable medium containing instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising: obtaining values of one or more process conditions including one or more process parameters, one or more metabolite concentrations and/or one or more biomass-related metrics for the bioprocess at one or more maturities; optionally wherein the system further comprises, in operable connection with the processor, one or more of: a user interface, wherein the instructions further cause the processor to provide, to the user interface for outputting to a user, one or more of: the value of the one or more specific transport rates or variables derived therefrom, the result of the comparison step, and a signal indicating that the bioprocess has been determined to operate normally or to not operate normally; one or more biomass sensor(s); one or more metabolite sensor(s); one or more process condition sensors; and one or more effector device(s).
Claim 16 recites the one or more values of process conditions used to predict the specific transport rates of the one or more metabolites include at least one metabolite concentration value, preferably wherein the one or more values of process conditions further includes at least one further value of a process condition, preferably at least two further values, and/or wherein the one or more values of metabolite concentrations include the concentration of one or more metabolites for which specific transport rates are determined.
There are no limitations that indicate that the claimed determining, comparing, and predicting 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. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-20 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 conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities. The instant claims recite the additional elements listed above.
As discussed above, there are no additional limitations to indicate that the claimed determining, comparing, and predicting require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea or natural law using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, the specification indicates it is typical (i.e., routine) for a bioreactor to be associated with instrumentation that continuously measures process conditions and includes samples of the culture taken periodically (Page 1, Lines 13-18). The specification also indicates multivariate statistical models have become a popular and commercially available (i.e., routine) tool for identifying process conditions that are important (Page 1, Lines 24-29). Kadlec et al. (2009, Computers and Chemical Engineering, Vol. 33: 795-814) also demonstrates that artificial neural networks are amongst the most popular (i.e. conventional) modeling techniques for estimating conditions within a bioreactor (Page 796, Column1, Paragraph 2).
The additional elements do not 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 1-20 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7-9, 11, 13-16, and 18-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Willson et al. (U.S. Pre-Grant Publication 20120107921). Italicized text from reference art.
Below the applicable claims are listed:
Claim 1. A method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising: i. obtaining values of one or more process conditions including one or more process parameters, one or more metabolite concentrations and/or one or more biomass-related metrics for the bioprocess at one or more maturities; ii. determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of said one or more process conditions for the bioprocess at the one or more maturities; and iii. predicting one or more features of the bioprocess based at least in part on the determined specific transport rates.
Claim 2. The method of claim 1, wherein predicting one or more features of the bioprocess comprises: i. comparing the specific transport rates or values derived therefrom to one or more predetermined values; and ii. determining on the basis of the comparison whether the process is operating normally.
Claim 3. The method of claim 1, wherein the specific transport rate of a metabolite is the net amount of the metabolite transported between the cells and the culture medium, per cell and per unit of maturity.
Claim 4. The method of claim 1, wherein the one or more process conditions include one or more process parameters selected from dissolved oxygen, dissolved C02, pH, temperature, osmolality, agitation speed, agitation power, headspace gas composition (such as e.g. CO2 pressure), flow rates (such as feed rate, bleed rate, harvest rate), feed medium composition and volume of the culture; wherein the one or more process conditions include one or more biomass related metrics selected from viable cell density, total cell density, cell viability, dead cell density, and lysed cell density; and/or wherein the one or more metabolite concentrations include the concentration of one or more metabolites in the cellular compartment, in the culture medium compartment, or in the cell culture as a whole.
Claim 5. The method of claim 1, wherein said values of one or more process conditions used to predict the specific transport rates of the one or more metabolites include at least one metabolite concentration value at the one or more maturities, preferably wherein the values of one or more process conditions further includes at least the value(s) of a further process condition at the one or more maturities, preferably at least the value(s) of two further process conditions at the one or more maturities, and/or wherein the one or more values of metabolite concentrations include the concentration of one or more metabolites for which specific transport rates are determined.
Claim 7. The method of claim 1, wherein the machine learning model is a regression model, or wherein the machine learning model is selected from a linear regression model, a random forest regressor, an artificial neural network (ANN), and a combination thereof; and or wherein the machine learning model comprises a plurality of machine learning models, wherein each machine learning model has been trained to predict the specific transport rates of an individually selected subset of the one or more metabolites.
Claim 8. The method of claim 1, wherein the machine learning model has been trained to jointly predict the specific transport rates of the one or more metabolites at a later maturity based at least in part on the values of one or more process conditions for the bioprocess at one or more preceding maturities.
Claim 9. The method of any claim 1, wherein obtaining values of one or more process conditions at one or more maturities comprises i. obtaining values of the one or more process conditions at a plurality of maturities; and ii. the machine learning model has been trained to predict the specific transport rates of the one or more metabolites at a latest of the plurality of maturities or a later maturity based at least in part on the values of one or more process conditions for the bioprocess at the plurality of maturities, optionally wherein the machine learning model has been trained to predict the specific transport rates of the one or more metabolites at a latest of two distinct maturities or a later maturity based at least in part on the values of one or more process conditions for the bioprocess at the two distinct maturities.
Claim 11. The method of claim 1, wherein predicting one or more features of the bioprocess comprises determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, optionally wherein determining the concentration of the corresponding one or more metabolites at the later maturity comprises solving respective material balance equations.
Claim 13. The method of claim 11, further comprising determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, and using one of more of said concentrations to determine the value of a biomass related metric at the later maturity, optionally wherein determining the value of a biomass related metric at the later maturity comprises solving a kinetic growth model, optionally further comprising using one or more of the said metabolite concentrations and/or biomass related metric values as inputs to the machine learning model to predict specific transport rates at a further maturity.
Claim 14. The method of claim 13, further comprising predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity.
Claim 15. A system for monitoring and/or controlling a bioprocess, the system including: i. at least one processor; and ii. at least one non-transitory computer readable medium containing instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising: iii. obtaining values of one or more process conditions including one or more process parameters, one or more metabolite concentrations and/or one or more biomass-related metrics for the bioprocess at one or more maturities; determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of said one or more process conditions for the bioprocess at the one or more maturities; and predicting one or more features of the bioprocess based at least in part on the determined specific transport rates; optionally wherein the system further comprises, in operable connection with the processor, one or more of: a user interface, wherein the instructions further cause the processor to provide, to the user interface for outputting to a user, one or more of: the value of the one or more specific transport rates or variables derived therefrom, the result of the comparison step, and a signal indicating that the bioprocess has been determined to operate normally or to not operate normally; one or more biomass sensor(s); one or more metabolite sensor(s); one or more process condition sensors; and one or more effector device(s).
Claim 16. The system of claim 15, wherein the one or more values of process conditions used to predict the specific transport rates of the one or more metabolites include at least one metabolite concentration value, preferably wherein the one or more values of process conditions further includes at least one further value of a process condition, preferably at least two further values, and/or wherein the one or more values of metabolite concentrations include the concentration of one or more metabolites for which specific transport rates are determined.
Claim 18. The system of claim 15, wherein predicting one or more features of the bioprocess comprises determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, optionally wherein determining the concentration of the corresponding one or more metabolites at the later maturity comprises solving respective material balance equations.
Claim 19. The system of claim 18, wherein the method further comprises determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, and using one of more of said concentrations to determine the value of a biomass related metric at the later maturity, optionally wherein determining the value of a biomass related metric at the later maturity comprises solving a kinetic growth model, optionally further comprising using one or more of the said metabolite concentrations and/or biomass related metric values as inputs to the machine learning model to predict specific transport rates at a further maturity.
Claim 20. The system of claim 19, wherein the method further comprises predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity.
Regarding Claim 1, Willson et al. teaches a method that includes (Claim 1.i ) obtaining values of one or more process conditions (Page 4, Paragraph 0063: The sensed, modeled, or hand-measured environmental conditions (or environmental inputs) may include a variety of parameters. Examples of environmental conditions include one or more of incident light such as photosynthetically active radiation, air and or water temperature, algal culture pH, dissolved oxygen, dissolved carbon, oxygen gas concentration, carbon gas concentration, dissolved carbon dioxide, algal culture density, and culture constituent levels). Willson et al. also teaches (Claim 1.ii) determining the specific transport rates of metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the metabolites at a latest maturity (Page 5, Paragraph 0068: The performance objectives of gas control system can include maximizing carbon dioxide utilization; Page 7, Paragraph 0093: Dynamic models (used to predict the values) can use one or more memory elements while some static models may be implemented without any stored values. Some examples include tap-delayed feedforward neural networks, recurrent neural networks, and echo state networks; Page 7, Paragraph 0095: Measurement of these state variables may be one method of validating, configuring, or calibrating the model). Neural networks are a category of machine learning models. Willson et al. also teaches (Claim 1.iii) predicting one or more features of the bioprocess based on the determined specific transport rates (Page 7, Paragraph 0098: the main goals of the model are to maximize growth (and hence CO2 uptake) in the first stage and storage lipid accumulation in the second).
Regarding Claim 2, Wilson et al. teaches comparing transport rates to one or more predetermined values (Page 4, Paragraph 0055: a harvesting module that is configured to calculate a harvest time at which a future growth of algae equals a predetermined threshold growth of algae). The growth rate of the algae was based from a metabolite transfer (Page 4, Paragraph 0054: The model can predict algae growth from the set of conditions and a set of input variable (e.g., carbon Supply rate)). Additionally, if the algae is ready for harvest, it must have grown (i.e., the process is operating normally).
Regarding Claim 3, Wilson et al. teaches wherein the specific transport rate of a metabolite is the net amount of the metabolite transported between the cells and the culture medium, per cell and per unit of maturity (Page 10, Paragraph 0129-0130: the growth rate may be a function of available light photons, available nutrients, dissolved CO2, dissolved O2, temperature, and media recipe (e.g., media pH). All of these can both be included in the model parameters considered here and be modeled as separate terms. The water chemistry subsystem models both the dissolved gases and nutrients available to the microalgae in the media).
Regarding Claim 4, Willson et al. teaches the specific process conditions (Page 4, Paragraph 0063: The sensed, modeled, or hand-measured environmental conditions (or environmental inputs) may include a variety of parameters. Examples of environmental conditions include one or more of incident light such as photosynthetically active radiation, air and or water temperature, algal culture pH, dissolved oxygen, dissolved carbon, oxygen gas concentration, carbon gas concentration, dissolved carbon dioxide, algal culture density, and culture constituent levels).
Regarding Claim 5, Wilson et al. teaches the values of the process conditions used to predict the specific transport rates of metabolites include metabolite concentration values at one or more maturities (Page 4, Paragraph 0063: The sensed, modeled, or hand-measured environmental conditions (or environmental inputs) may include a variety of parameters. Examples of environmental conditions include one or more of incident light such as photosynthetically active radiation, air and or water temperature, algal culture pH, dissolved oxygen, dissolved carbon, oxygen gas concentration, carbon gas concentration, dissolved carbon dioxide, algal culture density, and culture constituent levels). These values were used to predict CO2 utilization (Page 5, Paragraph 68).
Regarding claim 7, Willson et al. teaches the machine learning model is an artificial neural network (Page 5, Paragraph 68: The performance objectives of gas control system can include maximizing carbon dioxide utilization; Page 7, Paragraph 0093: Dynamic models (used to predict the values) can use one or more memory elements while some static models may be implemented without any stored values. Some examples include a tap-delayed feedforward neural networks, recurrent neural networks, and echo state networks).
Regarding Claim 8, Willson et al. teaches the machine learning model has been trained to jointly predict the specific transport rates of metabolites at a later maturity based on the values of process conditions for the bioprocess at one or more preceding maturities (Page 4, Paragraph 0055: a harvesting module that is configured to calculate a harvest time at which a future growth of algae equals a predetermined threshold growth of algae; page 4, Paragraph 0054: The model can predict algae growth from the set of conditions and a set of input variable (e.g., carbon supply rate)).
Regarding Claim 9, Willson et al. teaches (Claim 9.i) obtaining values of the process conditions at one or more maturities comprises obtaining values of the process conditions at a plurality of maturities (Page 14, Paragraph 0180: The outputs of the sub models provide calibration parameters. The real-time portion receives these calibration parameters once at the inception of the program and receives updated environmental data continuously via the loop returning. Each time updated environmental data is received, in the next step these inputs are used by the growth model to calculate CO2 required). Willson et al. also teaches (Claim 9.ii) the machine learning model has been trained to predict the specific transport rates of the one or more metabolites at a latest maturity or a later maturity based on the values of one or more process conditions for the bioprocess at the plurality of maturities Page 7, Paragraph 0095: Measurement of these state variables may be one method of validating, configuring, or calibrating the model; Page 7, Paragraph 0096: A model used for feed forward control should accurately model the requirements of the algae, such as the nutrients and amount of CO2 .
Regarding Claim 11, Willson et al. teaches predicting one or more features of the bioprocess comprises determining the value of variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding metabolites at the later maturity (Page 14, Paragraph 0180: The outputs of the sub models provide calibration parameters. The real-time portion receives these calibration parameters once at the inception of the program and receives updated environmental data continuously via the loop returning. Each time updated environmental data is received, in the next step these inputs are used by the growth model to calculate CO2 required).
Regarding Claim 13, Willson et al. teaches determining the value of variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding metabolites at the later maturity, and using the said concentrations to determine the value of a biomass related metric at the later maturity (Page 13, Paragraph 0225: Based on predicted photosynthetically active radiation, a CO2 prediction is calculated by the algal growth model (i.e., biomass)).
Regarding Claim 14, Willson et al. teaches predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity (Page 20, Paragraph 241: The dynamic process model employs signals from the environmental sensors and photobioreactors to simulate the relevant dynamics of the processes occurring within the photobioreactor that affect the parameters to be controlled. Relevant dynamics include light delivery to the active culture, gas transfer, algal photosynthesis, algal metabolism and nutrient uptake, algal culture hydrochemistry, and/or thermal behavior. The predictive feedforward controllers determine desired actuator behavior based on estimates of system parameters delivered by the dynamic process model). The predictive models include machine learning models (see regarding claim 7 of the current rejection).
Regarding Claim 15, Willson et al. teaches a system that includes (Claim 15.i) at least one processor and (Claim 15.ii) at least one non-transitory computer readable medium (Page 22, Paragraph 0256: Embodiments of the present invention may be provided as a computer program product that may include a machine-readable medium having stored thereon instructions that may be used to program a computer (or other electronic devices) to perform a process). Willson et al. also teaches (Claim 15.iii) the limitations of Claim 1 (Claim 1.i-iii) (See regarding claim 1 or the current rejection) .
Regarding Claim 16, Willson et al. teaches the values of process conditions used to predict the specific transport rates of the metabolites include at least one metabolite concentration value. This limitation is the same as claim 5 (see regarding claim 5 of the current rejection).
Regarding Claim 18, Willson et al. teaches predicting one or more features of the bioprocess comprises determining the value of variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding metabolites at the later maturity. This is the same limitation as claim 11 (see regarding claim 11 of the current rejection).
Regarding Claim 19, Willson et al. teaches determining the value of variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding metabolites at the later maturity, and using said concentrations to determine the value of a biomass related metric at the later maturity. This is the same limitation as claim 13 (see regarding claim 13 of the current rejection).
Regarding Claim 20, Willson et al teaches predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity. This is the same limitation as claim 14 (see regarding claim 14 of the current rejection).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-9, 11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Willson et al. (U.S. Pre-Grant Publication 20120107921), as applied to Claims 1-5, 7-9, 11, 13-16, and 18-20 in the 35 USC 102 rejection above, in view of Berry et al. (2016, Biotechnology Progress, Vol. 32, No. 1: 224-234). Italicized text from reference art.
Below the applicable claims are listed:
Claim 1. A method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising: i. obtaining values of one or more process conditions including one or more process parameters, one or more metabolite concentrations and/or one or more biomass-related metrics for the bioprocess at one or more maturities; ii. determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of said one or more process conditions for the bioprocess at the one or more maturities; and iii. predicting one or more features of the bioprocess based at least in part on the determined specific transport rates.
Claim 2. The method of claim 1, wherein predicting one or more features of the bioprocess comprises: i. comparing the specific transport rates or values derived therefrom to one or more predetermined values; and ii. determining on the basis of the comparison whether the process is operating normally.
Claim 3. The method of claim 1, wherein the specific transport rate of a metabolite is the net amount of the metabolite transported between the cells and the culture medium, per cell and per unit of maturity.
Claim 4. The method of claim 1, wherein the one or more process conditions include one or more process parameters selected from dissolved oxygen, dissolved C02, pH, temperature, osmolality, agitation speed, agitation power, headspace gas composition (such as e.g. CO2 pressure), flow rates (such as feed rate, bleed rate, harvest rate), feed medium composition and volume of the culture; wherein the one or more process conditions include one or more biomass related metrics selected from viable cell density, total cell density, cell viability, dead cell density, and lysed cell density; and/or wherein the one or more metabolite concentrations include the concentration of one or more metabolites in the cellular compartment, in the culture medium compartment, or in the cell culture as a whole.
Claim 5. The method of claim 1, wherein said values of one or more process conditions used to predict the specific transport rates of the one or more metabolites include at least one metabolite concentration value at the one or more maturities, preferably wherein the values of one or more process conditions further includes at least the value(s) of a further process condition at the one or more maturities, preferably at least the value(s) of two further process conditions at the one or more maturities, and/or wherein the one or more values of metabolite concentrations include the concentration of one or more metabolites for which specific transport rates are determined.
Claim 6. The method of claim 1, wherein predicting one or more features of the bioprocess comprising i. predicting the value of one or more critical quality attributes (CQAs) of the bioprocess ii. using a predictive model that has been trained to predict CQAs using a set of predictor variables comprising the one or more specific transport rates.
Claim 7. The method of claim 1, wherein the machine learning model is a regression model, or wherein the machine learning model is selected from a linear regression model, a random forest regressor, an artificial neural network (ANN), and a combination thereof; and or wherein the machine learning model comprises a plurality of machine learning models, wherein each machine learning model has been trained to predict the specific transport rates of an individually selected subset of the one or more metabolites.
Claim 8. The method of claim 1, wherein the machine learning model has been trained to jointly predict the specific transport rates of the one or more metabolites at a later maturity based at least in part on the values of one or more process conditions for the bioprocess at one or more preceding maturities.
Claim 9. The method of any claim 1, wherein obtaining values of one or more process conditions at one or more maturities comprises i. obtaining values of the one or more process conditions at a plurality of maturities; and ii. the machine learning model has been trained to predict the specific transport rates of the one or more metabolites at a latest of the plurality of maturities or a later maturity based at least in part on the values of one or more process conditions for the bioprocess at the plurality of maturities, optionally wherein the machine learning model has been trained to predict the specific transport rates of the one or more metabolites at a latest of two distinct maturities or a later maturity based at least in part on the values of one or more process conditions for the bioprocess at the two distinct maturities.
Claim 11. The method of claim 1, wherein predicting one or more features of the bioprocess comprises determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, optionally wherein determining the concentration of the corresponding one or more metabolites at the later maturity comprises solving respective material balance equations.
Claim 13. The method of claim 11, further comprising determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, and using one of more of said concentrations to determine the value of a biomass related metric at the later maturity, optionally wherein determining the value of a biomass related metric at the later maturity comprises solving a kinetic growth model, optionally further comprising using one or more of the said metabolite concentrations and/or biomass related metric values as inputs to the machine learning model to predict specific transport rates at a further maturity.
Claim 14. The method of claim 13, further comprising predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity.
Claim 15. A system for monitoring and/or controlling a bioprocess, the system including: i. at least one processor; and ii. at least one non-transitory computer readable medium containing instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising: iii. obtaining values of one or more process conditions including one or more process parameters, one or more metabolite concentrations and/or one or more biomass-related metrics for the bioprocess at one or more maturities; determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of said one or more process conditions for the bioprocess at the one or more maturities; and predicting one or more features of the bioprocess based at least in part on the determined specific transport rates; optionally wherein the system further comprises, in operable connection with the processor, one or more of: a user interface, wherein the instructions further cause the processor to provide, to the user interface for outputting to a user, one or more of: the value of the one or more specific transport rates or variables derived therefrom, the result of the comparison step, and a signal indicating that the bioprocess has been determined to operate normally or to not operate normally; one or more biomass sensor(s); one or more metabolite sensor(s); one or more process condition sensors; and one or more effector device(s).
Claim 16. The system of claim 15, wherein the one or more values of process conditions used to predict the specific transport rates of the one or more metabolites include at least one metabolite concentration value, preferably wherein the one or more values of process conditions further includes at least one further value of a process condition, preferably at least two further values, and/or wherein the one or more values of metabolite concentrations include the concentration of one or more metabolites for which specific transport rates are determined.
Claim 17. The system of claim 15, wherein predicting one or more features of the bioprocess comprises: i. comparing the specific transport rates or values derived therefrom to one or more predetermined values and determining on the basis of the comparison whether the process is operating normally; or ii. predicting the value of one or more critical quality attributes (CQAs) of the bioprocess iii. using a predictive model that has been trained to predict CQAs using a set of predictor variables comprising the one or more specific transport rates.
Claim 18. The system of claim 15, wherein predicting one or more features of the bioprocess comprises determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, optionally wherein determining the concentration of the corresponding one or more metabolites at the later maturity comprises solving respective material balance equations.
Claim 19. The system of claim 18, wherein the method further comprises determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, and using one of more of said concentrations to determine the value of a biomass related metric at the later maturity, optionally wherein determining the value of a biomass related metric at the later maturity comprises solving a kinetic growth model, optionally further comprising using one or more of the said metabolite concentrations and/or biomass related metric values as inputs to the machine learning model to predict specific transport rates at a further maturity.
Claim 20. The system of claim 19, wherein the method further comprises predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity.
Regarding Claim 1, Willson et al. teaches a method that includes (Claim 1.i ) obtaining values of one or more process conditions (Page 4, Paragraph 0063: The sensed, modeled, or hand-measured environmental conditions (or environmental inputs) may include a variety of parameters. Examples of environmental conditions include one or more of incident light such as photosynthetically active radiation, air and or water temperature, algal culture pH, dissolved oxygen, dissolved carbon, oxygen gas concentration, carbon gas concentration, dissolved carbon dioxide, algal culture density, and culture constituent levels). Willson et al. also teaches (Claim 1.ii) determining the specific transport rates of metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the metabolites at a latest maturity (Page 5, Paragraph 0068: The performance objectives of gas control system can include maximizing carbon dioxide utilization; Page 7, Paragraph 0093: Dynamic models (used to predict the values) can use one or more memory elements while some static models may be implemented without any stored values. Some examples include tap-delayed feedforward neural networks, recurrent neural networks, and echo state networks; Page 7, Paragraph 0095: Measurement of these state variables may be one method of validating, configuring, or calibrating the model). Neural networks are a category of machine learning models. Willson et al. also teaches (Claim 1.iii) predicting one or more features of the bioprocess based on the determined specific transport rates (Page 7, Paragraph 0098: the main goals of the model are to maximize growth (and hence CO2 uptake) in the first stage and storage lipid accumulation in the second).
Regarding Claim 2, Wilson et al. teaches comparing transport rates to one or more predetermined values (Page 4, Paragraph 0055: a harvesting module that is configured to calculate a harvest time at which a future growth of algae equals a predetermined threshold growth of algae). The growth rate of the algae was based from a metabolite transfer (Page 4, Paragraph 0054: The model can predict algae growth from the set of conditions and a set of input variable (e.g., carbon Supply rate)). Additionally, if the algae is ready for harvest, it must have grown (i.e., the process is operating normally).
Regarding Claim 3, Wilson et al. teaches wherein the specific transport rate of a metabolite is the net amount of the metabolite transported between the cells and the culture medium, per cell and per unit of maturity (Page 10, Paragraph 0129-0130: the growth rate may be a function of available light photons, available nutrients, dissolved CO2, dissolved O2, temperature, and media recipe (e.g., media pH). All of these can both be included in the model parameters considered here and be modeled as separate terms. The water chemistry subsystem models both the dissolved gases and nutrients available to the microalgae in the media).
Regarding Claim 4, Willson et al. teaches the specific process conditions (Page 4, Paragraph 0063: The sensed, modeled, or hand-measured environmental conditions (or environmental inputs) may include a variety of parameters. Examples of environmental conditions include one or more of incident light such as photosynthetically active radiation, air and or water temperature, algal culture pH, dissolved oxygen, dissolved carbon, oxygen gas concentration, carbon gas concentration, dissolved carbon dioxide, algal culture density, and culture constituent levels).
Regarding Claim 5, Wilson et al. teaches the values of the process conditions used to predict the specific transport rates of metabolites include metabolite concentration values at one or more maturities (Page 4, Paragraph 0063: The sensed, modeled, or hand-measured environmental conditions (or environmental inputs) may include a variety of parameters. Examples of environmental conditions include one or more of incident light such as photosynthetically active radiation, air and or water temperature, algal culture pH, dissolved oxygen, dissolved carbon, oxygen gas concentration, carbon gas concentration, dissolved carbon dioxide, algal culture density, and culture constituent levels). These values were used to predict CO2 utilization (Page 5, Paragraph 68).
Regarding Claim 6, Willson et al. teaches (Claim 6.ii) using a predictive model trained on transport rates (Page 7, Paragraph 0095: Measurement of these state variables (e.g. CO2 utilization) may be one method of validating, configuring, or calibrating the model).
Regarding claim 7, Willson et al. teaches the machine learning model is an artificial neural network (Page 5, Paragraph 68: The performance objectives of gas control system can include maximizing carbon dioxide utilization; Page 7, Paragraph 0093: Dynamic models (used to predict the values) can use one or more memory elements while some static models may be implemented without any stored values. Some examples include a tap-delayed feedforward neural networks, recurrent neural networks, and echo state networks).
Regarding Claim 8, Willson et al. teaches the machine learning model has been trained to jointly predict the specific transport rates of metabolites at a later maturity based on the values of process conditions for the bioprocess at one or more preceding maturities (Page 4, Paragraph 0055: a harvesting module that is configured to calculate a harvest time at which a future growth of algae equals a predetermined threshold growth of algae; page 4, Paragraph 0054: The model can predict algae growth from the set of conditions and a set of input variable (e.g., carbon supply rate)).
Regarding Claim 9, Willson et al. teaches (Claim 9.i) obtaining values of the process conditions at one or more maturities comprises obtaining values of the process conditions at a plurality of maturities (Page 14, Paragraph 0180: The outputs of the sub models provide calibration parameters. The real-time portion receives these calibration parameters once at the inception of the program and receives updated environmental data continuously via the loop returning. Each time updated environmental data is received, in the next step these inputs are used by the growth model to calculate CO2 required). Willson et al. also teaches (Claim 9.ii) the machine learning model has been trained to predict the specific transport rates of the one or more metabolites at a latest maturity or a later maturity based on the values of one or more process conditions for the bioprocess at the plurality of maturities Page 7, Paragraph 0095: Measurement of these state variables may be one method of validating, configuring, or calibrating the model; Page 7, Paragraph 0096: A model used for feed forward control should accurately model the requirements of the algae, such as the nutrients and amount of CO2 .
Regarding Claim 11, Willson et al. teaches predicting one or more features of the bioprocess comprises determining the value of variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding metabolites at the later maturity (Page 14, Paragraph 0180: The outputs of the sub models provide calibration parameters. The real-time portion receives these calibration parameters once at the inception of the program and receives updated environmental data continuously via the loop returning. Each time updated environmental data is received, in the next step these inputs are used by the growth model to calculate CO2 required).
Regarding Claim 13, Willson et al. teaches determining the value of variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding metabolites at the later maturity, and using the said concentrations to determine the value of a biomass related metric at the later maturity (Page 13, Paragraph 0225: Based on predicted photosynthetically active radiation, a CO2 prediction is calculated by the algal growth model (i.e., biomass)).
Regarding Claim 14, Willson et al. teaches predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity (Page 20, Paragraph 241: The dynamic process model employs signals from the environmental sensors and photobioreactors to simulate the relevant dynamics of the processes occurring within the photobioreactor that affect the parameters to be controlled. Relevant dynamics include light delivery to the active culture, gas transfer, algal photosynthesis, algal metabolism and nutrient uptake, algal culture hydrochemistry, and/or thermal behavior. The predictive feedforward controllers determine desired actuator behavior based on estimates of system parameters delivered by the dynamic process model). The predictive models include machine learning models (see regarding claim 7 of the current rejection).
Regarding Claim 15, Willson et al. teaches a system that includes (Claim 15.i) at least one processor and (Claim 15.ii) at least one non-transitory computer readable medium (Page 22, Paragraph 0256: Embodiments of the present invention may be provided as a computer program product that may include a machine-readable medium having stored thereon instructions that may be used to program a computer (or other electronic devices) to perform a process). Willson et al. also teaches (Claim 15.iii) the limitations of Claim 1 (Claim 1.i-iii) (See regarding claim 1 or the current rejection) .
Regarding Claim 16, Willson et al. teaches the values of process conditions used to predict the specific transport rates of the metabolites include at least one metabolite concentration value. This limitation is the same as claim 5 (see regarding claim 5 of the current rejection).
Regarding Claim 17, Willson et al. teaches (Claim 17.i) comparing the specific transport rates or values derived therefrom to one or more predetermined values and determining on the basis of the comparison whether the process is operating normally. This limitation is the same a claim 2 (see regarding claim 2 of the current rejection). Willson et al. also teaches (Claim 17.iii) using a predictive model that has been trained to predict CQAs using a set of predictor variables comprising the one or more specific transport rates (Page 7, Paragraph 0095: Measurement of these state variables (e.g. CO2 utilization) may be one method of validating, configuring, or calibrating the model).
Regarding Claim 18, Willson et al. teaches predicting one or more features of the bioprocess comprises determining the value of variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding metabolites at the later maturity. This is the same limitation as claim 11 (see regarding claim 11 of the current rejection).
Regarding Claim 19, Willson et al. teaches determining the value of variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding metabolites at the later maturity, and using said concentrations to determine the value of a biomass related metric at the later maturity. This is the same limitation as claim 13 (see regarding claim 13 of the current rejection).
Regarding Claim 20, Willson et al teaches predicting the effect of a particular value of a process parameter at a later maturity by including the particular value in the material balance equations, the kinetic growth model and/or the input values used by the machine learning model to predict specific transport rates at a further maturity. This is the same limitation as claim 14 (see regarding claim 14 of the current rejection).
Willson et al. does not teach predicting the value of one or more critical quality attributes (CQAs) of the bioprocess (Claim 6.i and 17.ii)
Regarding Claim 6, Berry et al. teaches (Claim 6.i) predicting the value of one or more critical quality attributes (CQAs) of the bioprocess (Page 229, Column 2, Paragraph 3: The percent of glycated antibody was determined by high resolution quadrupole time of flight mass spectrometry). Percent glycation was an estimate of product quality and a CQA (Page 225, Column 1, Paragraph 1: The concern that percent glycation could be considered a CQA for future programs motivated the development of a quickly applied yet controlled mitigation strategy).
Regarding Claim 17, Berry et al. teaches (Claim 17.ii) predicting the value of one or more critical quality attributes (CQAs) of the bioprocess. This limitation is the same a claim 6 (see regarding claim 6 of the current rejection).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Willson et al. with Berry et al. because both references consider product quality. Willson et al. suggests that attributes of product quality, such as product value, are important considerations for process model optimization (Page 6, Paragraph 0077: A controller according to such embodiments maintains a constant culture density, or follows a culture density command trajectory. Such a command may be based on many factors including product pricing). Berry et al. teaches the specific utilization of product optimizing process models to consider quality, through the critical quality attribute (CQA) of percent glycation, which can result in the loss of product function (Page 225, Column 1, Paragraph 2). Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to consider CQAs introduced by Berry et al. with the methods from Willson et al. indicated above.
Furthermore, one of ordinary skill in the art would predict that the method taught by Berry et al. could be readily added to the method of Willson et al. with a reasonable expectation of success because both are working within the same technological field, modeling cellular processes within bioreactors. Accordingly, claims 1-9, 11, and 13-20 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Claims 1-5, 7-11, 13-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Willson et al. (U.S. Pre-Grant Publication 20120107921), as applied to Claims 1-9, 11, and 13-20 in the current rejection above, in view of Rio-Chanona et al. (2018, AIChE Journal, Vol. 65, No. 3: 915-923). Italicized text from reference art.
Below the applicable claims are listed:
For claims 1-5, 7-9, 11, 13-16, and 18-20 see above in the 103 Rejection.
Claim 10. The method of claim 1, wherein the values of one or more process conditions used as input to the machine learning model are associated with a plurality of maturities that are separated from each other by a difference in maturity that is approximately equal to the difference in maturity between the values used to train the machine learning model.
Willson et al. teaches Claims 1-5, 7-9, 11, 13-16, and 18-20 (see above in the 103 rejection).
Willson et al. does not teach that the values used as input to the machine learning model are associated with maturities that are separated from each other by a difference that is similar to the difference in maturity used to train the machine learning model (Claim 10).
Regarding Claim 10, Rio-Chanona et al. teaches the values of the process conditions used as input to the machine learning model are associated with a plurality of maturities that are separated from each other by a difference in maturity that is equal to the difference in maturity used to train the machine learning model. The machine learning models of Rio-Chanona et al. were trained on data from bioreactors run in batches to predict the biomass of a bioreactor that was run in batches (i.e., the maturities used in training and predicting were identical) (Page 918, Column 1, Paragraph 4: Upon completion of the integrated models, they (the scenarios) were applied to a batch operation; Page 918, Column 2, Paragraph 2: For each scenario, approximately 9000 data points were generated, resulting in 360,000 data points for the surrogate model construction (i.e., the training data represented batch runs); Page 920, Column 1, Paragraph 4: (The trained model) was applied to predict untested behaviors of the system throughout a large solution space of design variables to seek the optimal solution for further PBR design and batch operation).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Willson et al. with Rio-Chanona et al. because Willson et al. teach the implementation of machine learning models (see regarding claim 7 of the 103 rejection) but does not specify the parameters of a training dataset used to train the models. Rio-Chanona et al. teaches specific details of the training dataset for the machine learning models that would be applicable to the machine learning models used in Willson et al. Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the methods from the references indicated above.
Furthermore, one of ordinary skill in the art would predict that the method taught by Rio-Chanona et al. could be readily added to the method of Willson et al. with a reasonable expectation of success because they both utilize machine learning methods to model cellular processes within bioreactors. Accordingly, claims 1-5, 7-11, 13-16, and 18-20 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Claims 1-5, 7-9, 11, 12-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Willson et al. (U.S. Pre-Grant Publication 20120107921), as applied to Claims 1-9, 11, and 13-20 in the 103 rejection above, in view of Dorka (2007, Thesis, University of Waterloo: 1-185). Italicized text from reference art.
Below the applicable claims are listed:
For claims 1-5, 7-9, 11, 13-16, and 18-20 see above in the 103 rejection.
Claim 12. The method of claim 11, wherein determining the concentration of a metabolite i at maturity k, where k is the maturity associated with the predicted specific transport rates, comprises integrating any of equations (4), (4a)-(4f) and (28) between a preceding maturity at which mi is known and maturity k.
Willson et al. teaches Claims 1-5, 7-9, 11, 13-16, and 18-20 (see above in the 103 rejection).
Willson et al. does not teach determining the concentration of a metabolite i at maturity k, where k is the maturity associated with the predicted specific transport rates, comprises integrating any of equations (4), (4a)-(4f) and (28) between a preceding maturity at which mi is known and maturity k (Claim 12).
Regarding Claim 12, Dorka teaches an equivalent equation to 4c (Page 63, Equation 4.1) to calculate the change in the concentration of a metabolite over time based on the transportation rate and viable cell concentration. Equation 4.1 of Dorka contains an additional variable for the culture time (t), which is present on both sides of the equation, indicating (t) will drop out for batch operations leaving an identical equation to 4c of the instant application.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Willson et al. with Dorka because Willson et al. teach modeling cellular metabolism (Page 20, Paragraph 0241: The dynamic process model employs signals from the environmental sensors and photobioreactors to simulate the relevant dynamics of the processes occurring within the photobioreactor that affect the parameters to be controlled. Relevant dynamics include light delivery to the active culture, gas transfer, algal photosynthesis, algal metabolism and nutrient uptake) but does not specify a specific equation to calculate the changes in metabolite concentrations. Dorka teaches a specific equation for calculating changes in a metabolite concentration over time, which are applicable to the metabolic modeling suggested by Willson et al. Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the methods from the references indicated above.
Furthermore, one of ordinary skill in the art would predict that the method taught by Dorka could be readily added to the method of Willson et al. with a reasonable expectation of success because they both are concerned with modeling cellular processes within bioreactors. Accordingly, claims 1-5, 7-9, 11-16, and 18-20 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2 and 4 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 13, and 15 of U.S. Patent No. 11542564 (Reference Patent). Although the claims at issue are not identical, they are not patentably distinct from each other.
Below are the applicable instant claims:
Instant Claim 1. A method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising: i. obtaining values of one or more process conditions including one or more process parameters, one or more metabolite concentrations and/or one or more biomass-related metrics for the bioprocess at one or more maturities; ii. determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of said one or more process conditions for the bioprocess at the one or more maturities; and iii. predicting one or more features of the bioprocess based at least in part on the determined specific transport rates.
Instant Claim 2. The method of claim 1, wherein predicting one or more features of the bioprocess comprises: comparing the specific transport rates or values derived therefrom to one or more predetermined values; and determining on the basis of the comparison whether the process is operating normally.
Instant Claim 4. The method of claim 1, wherein the one or more process conditions include one or more process parameters selected from dissolved oxygen, dissolved C02, pH, temperature, osmolality, agitation speed, agitation power, headspace gas composition (such as e.g. CO2 pressure), flow rates (such as feed rate, bleed rate, harvest rate), feed medium composition and volume of the culture; wherein the one or more process conditions include one or more biomass related metrics selected from viable cell density, total cell density, cell viability, dead cell density, and lysed cell density; and/or wherein the one or more metabolite concentrations include the concentration of one or more metabolites in the cellular compartment, in the culture medium compartment, or in the cell culture as a whole.
Reference Claim 1. A method for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor comprising: measuring process conditions and metabolite concentrations for the biological system as a function of time (Instant Claim 1.i); determining metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites (Instant Claim 1.ii); providing the process conditions and the metabolic rates to a hybrid system model configured to predict production of the biomaterial, the hybrid system model comprising: a kinetic growth model configured to estimate cell growth as a function of time; and a metabolic condition model based on metabolite specific consumption or secretion rates, select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state; and predicting an amount of the biomaterial based on the hybrid system model (Instant Claim 1.iii).
Reference Claim 13. The method of claim 1, further comprising: obtaining a test sample from the bioreactor; and determining whether the amount of the biomaterial in the test sample is within a range predicted by the hybrid system model (Instant Claim 2).
Reference Claim 15. The method of claim 1, wherein the process conditions include one or more of pH, temperature, dissolved oxygen, osmolality, process flow leaving the bioreactor, growth media, by-products, amino acids, metabolites, oxygen flow rate, nitrogen flow rate, carbon dioxide flow rate, air flow rate, and agitation rate (Instant Claim 4).
All limitations of Instant Claims 1-2 and 4 are therefore anticipated by Reference Claims 1, 13, and 15.
Claims 1-4 and 11-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-4 of copending Application No. 18027045 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other.
Below are the applicable instant claims:
Instant Claim 1. A method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising: i. obtaining values of one or more process conditions including one or more process parameters, one or more metabolite concentrations and/or one or more biomass-related metrics for the bioprocess at one or more maturities; ii. determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of said one or more process conditions for the bioprocess at the one or more maturities; and iii. predicting one or more features of the bioprocess based at least in part on the determined specific transport rates.
Instant Claim 2. The method of claim 1, wherein predicting one or more features of the bioprocess comprises: i. comparing the specific transport rates or values derived therefrom to one or more predetermined values; and ii. determining on the basis of the comparison whether the process is operating normally.
Instant Claim 3. The method of claim 1, wherein the specific transport rate of a metabolite is the net amount of the metabolite transported between the cells and the culture medium, per cell and per unit of maturity.
Instant Claim 4. The method of claim 1, wherein the one or more process conditions include one or more process parameters selected from dissolved oxygen, dissolved C02, pH, temperature, osmolality, agitation speed, agitation power, headspace gas composition (such as e.g. CO2 pressure), flow rates (such as feed rate, bleed rate, harvest rate), feed medium composition and volume of the culture; wherein the one or more process conditions include one or more biomass related metrics selected from viable cell density, total cell density, cell viability, dead cell density, and lysed cell density; and/or wherein the one or more metabolite concentrations include the concentration of one or more metabolites in the cellular compartment, in the culture medium compartment, or in the cell culture as a whole.
Instant Claim 11. The method of claim 1, wherein predicting one or more features of the bioprocess comprises determining the value of one or more variables derived from the specific transport rates by: using the specific transport rates to determine the concentration of the corresponding one or more metabolites at the later maturity, optionally wherein determining the concentration of the corresponding one or more metabolites at the later maturity comprises solving respective material balance equations.
Instant Claim 12. The method of claim 11, wherein determining the concentration of a metabolite i at maturity k, where k is the maturity associated with the predicted specific transport rates, comprises integrating any of equations (4), (4a)-(4f) and (28) between a preceding maturity at which mi is known and maturity k.
Refence Claim 1. A computer implemented method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method including the steps of:
obtaining measurements of the amount of biomass and the amount of one or more metabolites in the bioreactor as a function of bioprocess maturity (Instant Claim 1.i);
determining one or more metabolic condition variables selected from: the specific transport rates between the cells and a culture medium in the bioreactor for some or all of the one or more metabolites as a function of bioprocess maturity, the internal concentration of one or more metabolites as a function of bioprocess maturity, and reaction rates for one or more metabolic reactions that form part of the cell's metabolism as a function of bioprocess maturity (Instant Claim 1.ii);
using a pre-trained multivariate model to determine the value of one or more latent variables as a function of bioprocess maturity, wherein the multivariate model is a linear model that uses process variables including the metabolic condition variables as predictor variables (Instant Claim 1.iii and Claim 11);
comparing the value(s) of the one or more latent variables to one or more predetermined values as a function of maturity (Instant Claim 2.i); and
determining on the basis of the comparison whether the bioprocess is operating normally (Instant Claim 2.ii).
Reference Claim 2. The method of claim 1, wherein determining one or more metabolic condition variables comprises determining the specific transport rate of the one or more metabolites between the cells and the culture medium, wherein the specific transport rate of a metabolite i is the amount of the metabolite transported between the cells and the culture medium, per cell and per unit of maturity, optionally wherein the specific transport rate of a metabolite i at a particular maturity m is determined using equation (7): [total change of metabolite amount in reactor] = [total flow of metabolite into reactor] - [total flow of metabolite out of reactor] + [secretion of metabolite by cells in reactor] - [consumption of metabolite by cells in reactor ] (7) (Instant Claim 12). Equation 7 of the reference application is the same as equation 3 of the instant application. Equation 4 of the instant application (cited in instant claim 12) is an obvious derivation of instant equation 3 (reference equation 7) according to the instant specification (Page 30, Lines 19-21: Therefore, the material balance described in equation (3) can be written for a general system, for metabolite i as equation (4) below).
Reference Claim 3. The method of claim 2, wherein the specific transport rate of a metabolite is a specific consumption rate or a specific production rate (Instant Claim 3).
Reference Claim 4. The method of claim 1, wherein measurements of the amount of biomass in the bioreactor comprise measurements of the viable cell density, and/or wherein measurements of the amount of one or more metabolites in the bioreactor comprise measurements of the amount or concentration of one or more metabolites in the cellular compartment, in the culture medium compartment, or in the cell culture as a whole (Instant Claim 4).
All limitations of Instant Claims 1-4 and 11-12 are therefore anticipated by Reference Claims 1-4. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1, 4 and 6 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-2, 6, and 8 of copending Application No. 18574469 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other.
Below are the applicable instant claims:
Instant Claim 1. A method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising: i. obtaining values of one or more process conditions including one or more process parameters, one or more metabolite concentrations and/or one or more biomass-related metrics for the bioprocess at one or more maturities; ii. determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of said one or more process conditions for the bioprocess at the one or more maturities; and iii. predicting one or more features of the bioprocess based at least in part on the determined specific transport rates.
Instant Claim 4. The method of claim 1, wherein the one or more process conditions include one or more process parameters selected from dissolved oxygen, dissolved C02, pH, temperature, osmolality, agitation speed, agitation power, headspace gas composition (such as e.g. CO2 pressure), flow rates (such as feed rate, bleed rate, harvest rate), feed medium composition and volume of the culture; wherein the one or more process conditions include one or more biomass related metrics selected from viable cell density, total cell density, cell viability, dead cell density, and lysed cell density; and/or wherein the one or more metabolite concentrations include the concentration of one or more metabolites in the cellular compartment, in the culture medium compartment, or in the cell culture as a whole.
Instant Claim 6. The method of claim 1, wherein predicting one or more features of the bioprocess comprising predicting the value of one or more critical quality attributes (CQAs) of the bioprocess using a predictive model that has been trained to predict CQAs using a set of predictor variables comprising the one or more specific transport rates.
Reference Claim 1. A computer-implemented method for monitoring a bioprocess comprising a cell culture in a bioreactor, the method comprising:
obtaining the values of one or more state variables of a state space model and optionally one or more variables derived therefrom, at one or more maturities, the state space model comprising a kinetic growth model representing changes in the state of the cell culture and optionally a material balance model representing changes in the bulk concentration of one or more metabolites in the bioreactor (Instant Claim 1.i-ii); and
predicting the value of one or more critical quality attributes of a product of the bioprocess using a machine learning model trained to predict the value of the one or more critical quality attributes based on input variables comprising values of the one or more state variables or variables derived therefrom, at one or more maturities (Instant Claim 1.iii and Claim 6).
Reference Claim 2. The method of claim 1, wherein the method further comprises obtaining values of one or more process conditions including one or more process parameters and/or one or more metabolite concentrations at one or more maturities and the input variables further comprise values of the one or more process conditions, and/or wherein the input variables comprise values of at least one of the one or more state variables (Instant Claim 1.i).
Reference Claim 6. (Currently amended) The method of claim 2, wherein the one or more process conditions include one or more process parameters selected from dissolved oxygen, dissolved CO2, pH, temperature, osmolality, agitation speed, agitation power, headspace gas composition (such as e.g. CO2 pressure), flow rates (such as feed rate, bleed rate, harvest rate), feed medium composition and volume of the culture; and/or wherein the one or more metabolite concentrations include the concentration of one or more metabolites in the cellular compartment, in the culture medium compartment, or in the cell culture as a whole (Instant Claim 4).
Reference Claim 8. The method of claim 1, wherein the machine learning model is a regression model, or wherein the machine learning model is selected from a linear regression model, a random forest regressor, an artificial neural network (ANN), and a combination thereof, suitably wherein the machine learning model is an artificial neural network; and/or wherein the machine learning model comprises a plurality of machine learning models, wherein each machine learning model has been trained to predict the values of an individually selected subset of the one or more critical quality attributes; and/or wherein the machine learning model has been trained to jointly predict value of the one or more critical quality attributes (Instant Claim 6).
All limitations of Instant Claims 1, 4 and 6 are therefore anticipated by Reference Claims 1-2, 6, and 8. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
No Claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BLAKE H ELKINS whose telephone number is (571)272-2649. The examiner can normally be reached Monday-Friday 8-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687