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 .
Remarks
In response to communications sent August 19, 2022, claim(s) 1-17 are pending in this application; of these claims 1, 8, and 10 are in independent form.
Response to Amendment
The preliminary amendments filed August 19, 2022 are acknowledged and have been entered into the record.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
At this point in prosecution, the claims are being given the filing date of the foreign priority document, EP 20290024.7 filed February 21, 2020. This analysis is based on a cursory comparison of the figures, claims, and specification sections of the priority document to the instant application.
Drawings
The drawings are objected to because there is a typographical error in element S20 of Figure 2, because “paramters” is misspelled. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: August 19, 2022 and August 1, 2025.
Claim Objections
Claim 7 is objected to because of the following informalities: The claim does not have a period because the period was deleted in the most recent amendment. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: The claim does not have a period. 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 5 and 14 recites the limitation "the disintegration of the dead cells" in lines 1-2 in the respective claims. There is insufficient antecedent basis for this limitation in the claim. The Examiner suggests amending claim 4 to depend from claim 3 so that claim 5 depends from claim 3 as well. The Examiner suggests amending claim 13 to depend from claim 12 so that claim 14 depends from claim 12 as well.
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.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to software per se. The claims recite a “computer program product comprising computer-readable instructions…” Furthermore, the “computer program product” and “computer-readable instructions” do not have specialized definitions in Applicant’s Specification. Therefore, the claims are directed to software per se, which does not fall within any of the four statutory classes.
See also the rejection of claim 1 below regarding judicial exceptions, which may be pertinent to claim 9, should claim 9 be amended to recite a statutory class.
Claim 1-8 and 10-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematics, a category of abstract ideas. This judicial exception is not integrated into a practical application because applying mathematics on a general purpose computer, including storing information, is not an integration into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these same additional elements are either the application of mathematics on a general purpose computer (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 216, 110 USPQ2d 1976, 1980 (2014)); or the additional elements include storage of information (see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93)).
(Note that claims 16 and 17 are not rejected under 35 U.S.C. § 101 in view of Diamond v. Diehr, 450 U.S. 175, 191-92 n.14, 209 USPQ 1, 10-11 n.14 (1981) ).
1. A computer-implemented method for simulating a cell culture process, comprising:
obtaining measurable parameter values that are measured with respect to at least one operation of the cell culture process, the measurable parameter values being values of measurable parameters in a model of the cell culture process, wherein one or more of the measurable parameters relate to one or more operating conditions of the cell culture process (a computer-implemented, i.e., in silico, step of obtaining in silico parameters, which is a mathematical step; mathematics is an abstract idea);
estimating, using Bayesian inference with the obtained measurable parameter values, values of unmeasurable parameters in the model, wherein the model describes the cell culture process with coupled ordinary differential equations including the measurable parameters and the unmeasurable parameters, wherein one or more of the unmeasurable parameters relate to lysed cells in the cell culture process (mathematics, which is an abstract idea);
receiving one or more new measurable parameter values relating to said one or more operating conditions of the cell culture process (a computer-implemented, i.e., in silico, step of receiving in silico parameter value, which is a mathematical step; mathematics is an abstract idea);
simulating the cell culture process (simulation of this type uses differential equations according to the specification, which is mathematics and an abstract idea) using:
the model of the cell culture process (a mathematical model of the cell culture, which the specification describes as using differential equations, which is mathematics);
the estimated values of the unmeasurable parameters in the model (a mathematical estimation, which the specification describes as using differential equations, which is mathematics); and
the received one or more new measurable parameter values (a computer-implemented, i.e., in silico, step of receiving in silico parameter value, which is a mathematical step; mathematics is an abstract idea).
2. The method according to claim 1, wherein said one or more of the unmeasurable parameters include concentration of the lysed cells in the cell culture process and/or concentration of a biomaterial that has a toxic influence on the viable cells (these are limitations to the mathematical idea itself, since the values are in silico).
3. The method according to claim 2, wherein the concentration of the lysed cells is tracked through modeling disintegration of dead cells (according the Specification, these are implemented using differential equations, which are mathematics and abstract ideas).
4. The method according to claim 2, wherein production of a toxic biomaterial is modeled as a function of viable cell density (according the Specification, these are implemented using differential equations, which are mathematics and abstract ideas).
5. The method according to claim 4, wherein the modeling of the disintegration of the dead cells involves cell death rate that is adjusted by the concentration of the lysed cells and/or concentration of the toxic biomaterial (according the Specification, these are implemented using differential equations, which are mathematics and abstract ideas).
6. The method according to claim 1, further comprising:
receiving information indicating desired cell growth in the cell culture process (a computer-implemented, i.e., in silico, step of receiving an in silico value, which is a mathematical step; mathematics is an abstract idea); and
determining, according to a result of said simulating the cell culture process, optimal operating conditions for obtaining the desired cell growth (according the Specification, these are implemented using differential equations, which are mathematics and abstract ideas).
7. The method according to claim 1, further comprising:
storing the estimated values of the unmeasurable parameters in a storage medium (this is an additional element, beyond the abstract idea; however storing information on a general purpose computer; this is not integrated into a practical application, because it amounts to “applying” the simulation on a general purpose computer; and courts have determined that instructions to store information is well-understood, routine, and conventional; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
8. A computer-implemented method for simulating a cell culture process, comprising:
receiving one or more measurable parameter values relating to one or more operating conditions of the cell culture process (a computer-implemented, i.e., in silico, step of receiving in silico parameter value, which is a mathematical step; mathematics is an abstract idea); and
simulating the cell culture process (simulation of this type uses differential equations according to the specification, which is mathematics and an abstract idea) using:
a model of the cell culture process (a mathematical model of the cell culture, which the specification describes as using differential equations, which is mathematics);
estimated values of unmeasurable parameters in the model (a mathematical estimation, which the specification describes as using differential equations, which is mathematics); and
the received one or more measurable parameter values (a computer-implemented, i.e., in silico, step of receiving in silico parameter value, which is a mathematical step; mathematics is an abstract idea),
wherein the model of the cell culture process describes the cell culture process with coupled ordinary differential equations including measurable parameters and the unmeasurable parameters, one or more of the measurable parameters relating to said one or more operating conditions of the cell culture process (mathematics, which is an abstract idea);
wherein one or more of the unmeasurable parameters relate to lysed cells in the cell culture process (these are limitations to the mathematical idea itself, since the parameters are in silico).; and
wherein the estimated values of the unmeasurable parameters are estimated using Bayesian inference with measurable parameter values that are measured with respect to at least one operation of the cell culture process (mathematics, which is an abstract idea).
10. A system for simulating a cell culture process, comprising:
a storage medium (this is an additional element, beyond the abstract idea; however storing information on a general purpose computer; this is not integrated into a practical application, because it amounts to “applying” the simulation on a general purpose computer; and courts have determined that instructions to store information is well-understood, routine, and conventional; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93) storing a model of the cell culture process, the model describing the cell culture process with coupled ordinary differential equations including measurable parameters and unmeasurable parameters, wherein one or more of the measurable parameters relate to one or more operating conditions of the cell culture process and one or more of the unmeasurable parameters relate to lysed cells in the cell culture process (mathematics, which is an abstract idea implemented on a general-purpose computer); and
a processor configured (this is an additional element, beyond the abstract idea, but is part of a general-purpose computer; hence, it is not integrated into a practical application because it amounts to applying the abstract idea on a general-purpose computer, either alone or in combination with the storage medium) to:
obtain measurable parameter values that are measured with respect to at least one operation of the cell culture process, the measurable parameter values being values of the measurable parameters in the model (a computer-implemented, i.e., in silico, step of obtaining in silico parameters, which is a mathematical step; mathematics is an abstract idea);
estimate, using Bayesian inference with the obtained measurable parameter values, values of the unmeasurable parameters in the model (mathematics, which is an abstract idea);
receive one or more new measurable parameter values relating to said one or more operating conditions of the cell culture process (a computer-implemented, i.e., in silico, step of receiving in silico parameter value, which is a mathematical step; mathematics is an abstract idea);
simulate the cell culture process (simulation of this type uses differential equations according to the specification, which is mathematics and an abstract idea) using:
the model of the cell culture process (a mathematical model of the cell culture, which the specification describes as using differential equations, which is mathematics);
the estimated values of the unmeasurable parameters in the model (a mathematical estimation, which the specification describes as using differential equations, which is mathematics); and
the received one or more new measurable parameter values (a computer-implemented, i.e., in silico, step of receiving in silico parameter value, which is a mathematical step; mathematics is an abstract idea).
11. The system according to claim 10, wherein said one or more of the unmeasurable parameters include concentration of the lysed cells in the cell culture process and/or concentration of a biomaterial that has a toxic influence on the viable cells (these are limitations to the mathematical idea itself, since the values are in silico).
12. The system according to claim 11, wherein the concentration of the lysed cells is tracked through modeling disintegration of dead cells (according the Specification, these are implemented using differential equations, which are mathematics and abstract ideas).
13. The system according to claim 11, wherein production of a toxic biomaterial is modeled as a function of viable cell density (according the Specification, these are implemented using differential equations, which are mathematics and abstract ideas).
14. The system according to claim 13, wherein the modeling of the disintegration of the dead cells involves cell death rate that is adjusted by the concentration of the lysed cells and/or concentration of the toxic biomaterial (according the Specification, these are implemented using differential equations, which are mathematics and abstract ideas).
15. The system according to claim 10, wherein the processor is further configured to:
receive information indicating desired cell growth in the cell culture process (a computer-implemented, i.e., in silico, step of receiving an in silico value, which is a mathematical step; mathematics is an abstract idea); and
determine, according to a result of said simulating the cell culture process, optimal operating conditions for obtaining the desired cell growth (according the Specification, these are implemented using differential equations, which are mathematics and abstract ideas).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Rodríguez”. The reference Rodríguez is: Hernández Rodríguez, Tanja, et al. "Predicting industrial‐scale cell culture seed trains." Biotechnology and bioengineering 116.11 (2019): 2944-2959.
As to claim 1, Rodríguez teaches a computer-implemented method for simulating a cell culture process, comprising:
obtaining measurable parameter values that are measured with respect to at least one operation of the cell culture process (Rodríguez p. 2948 section 2.5: obtaining the measurable parameter values for maximum growth rate µmax), the measurable parameter values being values of measurable parameters in a model of the cell culture process (Rodríguez p. 2951 Table 3A: µmax is a parameter of the cell culture process), wherein one or more of the measurable parameters relate to one or more operating conditions of the cell culture process (Rodríguez p. 2947 section 2.4: the µmax parameter relates to other operating conditions such as the growth rate µ and various metabolite concentrations in the equation for µ);
estimating, using Bayesian inference with the obtained measurable parameter values, values of unmeasurable parameters in the model (Rodríguez p. 2948 section 2.5: paragraph 1: using Bayesian inference to computer parameter values), wherein the model describes the cell culture process with coupled ordinary differential equations including the measurable parameters and the unmeasurable parameters (Rodríguez p. 2947 section 2.4: the equations specify a differential-equation model with various parameters that must be optimized and are not measured and some, such as µ, which are measurable) wherein one or more of the unmeasurable parameters relate to lysed cells in the cell culture process (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the KLys is the cell lysis constant);
receiving one or more new measurable parameter values relating to said one or more operating conditions of the cell culture process (Rodríguez p. 2949 section 2.5.4: updating new parameters using Bayesian updating of the model, which relates to the operating conditions such as metabolite concentrations, etc.);
simulating the cell culture process (Rodríguez p. 2949 section 2.5.4: simulation using the updated model) using:
the model of the cell culture process (Rodríguez p. 2949 section 2.5.4: simulation using the posterior model);
the estimated values of the unmeasurable parameters in the model (Rodríguez p. 2949 section 2.5.4: simulation using the updated parameter estimates in the model); and
the received one or more new measurable parameter values (Rodríguez p. 2949 section 2.5.4: updating using new measured data to update the posterior model).
As to claim 2, Rodríguez teaches the method according to claim 1, wherein said one or more of the unmeasurable parameters include concentration of the lysed cells in the cell culture process (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the KLys is the cell lysis constant) and/or concentration of a biomaterial that has a toxic influence on the viable cells (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the qAmm variable for the production of ammonia, a toxic biomaterial, is modeled in an integrative model with variables, parameters, and equations for cell lysis and cell viability).
As to claim 3, Rodríguez teaches the method according to claim 2, wherein the concentration of the lysed cells is tracked through modeling disintegration of dead cells (Table 2 and the “Cell Culture Model” section 2.4 on page 2947; the KLys is the cell lysis constant is influenced by integration of µd , the disintegration of dead cells).
As to claim 4, Rodríguez teaches the method according to claim 2, wherein production of a toxic biomaterial is modeled as a function of viable cell density (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the qAmm variable for the production of ammonia, a toxic biomaterial, is modeled in an integrative model with variables, parameters, and equations for cell lysis and cell viability).
As to claim 5, Rodríguez teaches the method according to claim 4, wherein the modeling of the disintegration of the dead cells involves cell death rate that is adjusted by the concentration of the lysed cells and/or concentration of the toxic biomaterial (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the KLys is the cell lysis constant).
As to claim 6, Rodríguez teaches the method according to claim 1, further comprising:
receiving information indicating desired cell growth in the cell culture process (Rodríguez p. 2958: “transfer of one plant toa similar plant”; in this case, “plant” is a term of art in bioprocessing and cell culturing); and
determining, according to a result of said simulating the cell culture process, optimal operating conditions for obtaining the desired cell growth (Rodríguez p. 2958: “design of robust and optimal seed train protocols”).
As to claim 7, Rodríguez teaches the method according to claim 1, further comprising:
storing the estimated values of the unmeasurable parameters (Rodríguez Table 3A on page 2951: model parameters that are estimated instead of measured) in a storage medium (Rodríguez Figure 2 on page 2952: storing estimates of parameters using probability distributions, i.e. after optimization of the unmeasured parameters).
As to claim 8, Rodríguez teaches a computer-implemented method for simulating a cell culture process, comprising:
receiving one or more measurable parameter values relating to one or more operating conditions of the cell culture process (Rodríguez p. 2948 section 2.5: obtaining the measurable parameter values for maximum growth rate µmax; Rodríguez p. 2951 Table 3A: µmax is a parameter of the cell culture process); and
simulating the cell culture process (Rodríguez p. 2949 section 2.5.4: simulation using the updated model) using:
a model of the cell culture process (Rodríguez p. 2949 section 2.5.4: simulation using the posterior model);
estimated values of unmeasurable parameters in the model (Rodríguez p. 2949 section 2.5.4: simulation using the updated parameter estimates in the model); and
the received one or more measurable parameter values (Rodríguez p. 2949 section 2.5.4: updating using new measured data to update the posterior model),
wherein the model of the cell culture process describes the cell culture process with coupled ordinary differential equations including measurable parameters and the unmeasurable parameters Rodríguez p. 2947 section 2.4: the equations specify a differential-equation model with various parameters that must be optimized and are not measured and some, such as µ, which are measurable), one or more of the measurable parameters relating to said one or more operating conditions of the cell culture process (Rodríguez p. 2947 section 2.4: the µmax parameter relates to other operating conditions such as the growth rate µ and various metabolite concentrations in the equation for µ);
wherein one or more of the unmeasurable parameters relate to lysed cells in the cell culture process (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the KLys is the cell lysis constant); and
wherein the estimated values of the unmeasurable parameters are estimated using Bayesian inference with measurable parameter values (Rodríguez p. 2948 section 2.5: paragraph 1: using Bayesian inference to computer parameter values) that are measured with respect to at least one operation of the cell culture process (Rodríguez p. 2948 section 2.5: obtaining the measurable parameter values for maximum growth rate µmax).
As to claim 9, Rodríguez teaches a computer program product comprising computer-readable instructions that, when loaded and run on a computer, cause the computer to perform the method according to claim 1 (Rodríguez p. 2948 section 2.5: obtaining the measurable parameter values for maximum growth rate µmax; Rodríguez p. 2951 Table 3A: µmax is a parameter of the cell culture process; Rodríguez p. 2947 section 2.4: the µmax parameter relates to other operating conditions such as the growth rate µ and various metabolite concentrations in the equation for µ; Rodríguez p. 2948 section 2.5: paragraph 1: using Bayesian inference to computer parameter values; Rodríguez p. 2947 section 2.4: the equations specify a differential-equation model with various parameters that must be optimized and are not measured and some, such as µ, which are measurable; Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the KLys is the cell lysis constant; Rodríguez p. 2949 section 2.5.4: updating new parameters using Bayesian updating of the model, which relates to the operating conditions such as metabolite concentrations, etc.; Rodríguez p. 2949 section 2.5.4: simulation using the updated model; Rodríguez p. 2949 section 2.5.4: simulation using the posterior model; Rodríguez p. 2949 section 2.5.4: simulation using the updated parameter estimates in the model; Rodríguez p. 2949 section 2.5.4: updating using new measured data to update the posterior model).
As to claim 10, Rodríguez teaches a system for simulating a cell culture process, comprising:
a storage medium storing a model of the cell culture process, the model describing the cell culture process with coupled ordinary differential equations including measurable parameters and unmeasurable parameters (Rodríguez p. 2947 section 2.4: the equations specify a differential-equation model with various parameters that must be optimized and are not measured and some, such as µ, which are measurable), wherein one or more of the measurable parameters relate to one or more operating conditions of the cell culture process (Rodríguez p. 2947 section 2.4: the µmax parameter relates to other operating conditions such as the growth rate µ and various metabolite concentrations in the equation for µ) and one or more of the unmeasurable parameters relate to lysed cells in the cell culture process (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the KLys is the cell lysis constant); and
a processor configured to:
obtain measurable parameter values that are measured with respect to at least one operation of the cell culture process (Rodríguez p. 2948 section 2.5: obtaining the measurable parameter values for maximum growth rate µmax), the measurable parameter values being values of the measurable parameters in the model (Rodríguez p. 2951 Table 3A: µmax is a parameter of the cell culture process);
estimate, using Bayesian inference with the obtained measurable parameter values, values of the unmeasurable parameters in the model (Rodríguez p. 2948 section 2.5: paragraph 1: using Bayesian inference to computer parameter values);
receive one or more new measurable parameter values relating to said one or more operating conditions of the cell culture process (Rodríguez p. 2949 section 2.5.4: updating new parameters using Bayesian updating of the model, which relates to the operating conditions such as metabolite concentrations, etc.);
simulate the cell culture process (Rodríguez p. 2949 section 2.5.4: simulation using the updated model) using:
the model of the cell culture process (Rodríguez p. 2949 section 2.5.4: simulation using the posterior model);
the estimated values of the unmeasurable parameters in the model (Rodríguez p. 2949 section 2.5.4: simulation using the updated parameter estimates in the model); and
the received one or more new measurable parameter values (Rodríguez p. 2949 section 2.5.4: updating using new measured data to update the posterior model).
As to claim 11, Rodríguez teaches the system according to claim 10, wherein said one or more of the unmeasurable parameters include concentration of the lysed cells in the cell culture process (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the KLys is the cell lysis constant) and/or concentration of a biomaterial that has a toxic influence on the viable cells (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the qAmm variable for the production of ammonia, a toxic biomaterial, is modeled in an integrative model with variables, parameters, and equations for cell lysis and cell viability).
As to claim 12, Rodríguez teaches the system according to claim 11, wherein the concentration of the lysed cells is tracked through modeling disintegration of dead cells (Table 2 and the “Cell Culture Model” section 2.4 on page 2947; the KLys is the cell lysis constant is influenced by integration of µd , the disintegration of dead cells).
As to claim 13, Rodríguez teaches the system according to claim 11, wherein production of a toxic biomaterial is modeled as a function of viable cell density (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the qAmm variable for the production of ammonia, a toxic biomaterial, is modeled in an integrative model with variables, parameters, and equations for cell lysis and cell viability).
As to claim 14, Rodríguez teaches the system according to claim 13, wherein the modeling of the disintegration of the dead cells involves cell death rate that is adjusted by the concentration of the lysed cells and/or concentration of the toxic biomaterial (Rodríguez Table 2 and the “Cell Culture Model” section 2.4 on page 2947: the KLys is the cell lysis constant).
As to claim 15, Rodríguez teaches the system according to claim 10, wherein the processor is further configured to:
receive information indicating desired cell growth in the cell culture process (Rodríguez p. 2958: “transfer of one plant toa similar plant”; in this case, “plant” is a term of art in bioprocessing and cell culturing); and
determine, according to a result of said simulating the cell culture process, optimal operating conditions for obtaining the desired cell growth (Rodríguez p. 2958: “design of robust and optimal seed train protocols”).
As to claim 16, Rodríguez teaches the method according to claim 6, further comprising:
generating one or more control signals for controlling one or more devices that carry out the cell culture process to operate under the determined optimal operating conditions (Rodríguez p. 2958 last paragraph: feed-forward control strategies); and
outputting the generated one or more control signals (Rodríguez p. 2958 last paragraph: bioprocessing using robust protocols using the computational method; control signals to control the bioprocessing using the computerized system are at once envisaged).
As to claim 17, Rodríguez teaches the system according to claim 15, wherein the processor is configured to:
generate one or more control signals for controlling one or more devices that carry out the cell culture process to operate under the determined optimal operating conditions (Rodríguez p. 2958 last paragraph: feed-forward control strategies); and
output the generated one or more control signals (Rodríguez p. 2958 last paragraph: bioprocessing using robust protocols using the computational method; control signals to control the bioprocessing using the computerized system are at once envisaged)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Klein, Tobias, et al. "Quantification of cell lysis during CHO bioprocesses: Impact on cell count, growth kinetics and productivity." Journal of Biotechnology 207 (2015): 67-76. (Year: 2015)
US 20220195369 A1: Pertinent because of the use of Bayesian optimization
US 12416902 B2: Pertinent because of the Bayesian model
US 20230077294 A1: Same Applicant as instant Application
US 20210262047 A1: Same Applicant as instant Application
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/JESSE P FRUMKIN/ Primary Examiner, Art Unit 1685 April 24, 2026
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