DETAILED ACTION
Applicant’s response filed 11/24/2025 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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 .
Status of the Claims
Claims 1-2 and 5-16 are pending and under consideration in this action. Claims 3-4 were canceled in the amendment filed 11/24/2025.
Priority
The instant application is a continuation of PCT/EP2020/072560, filed 8/12/2020, which claims priority to EPO Application Number 19191807.7, filed 8/14/2019, as reflected in the filing receipt mailed on 06/22/2022. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-2 and 5-16 is 8/14/2019.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/24/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code (see Pg. 3, Line 3). Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Response to Specification Amendments
Applicant argues that the specification has been amended at page 3, lines 3-5 of the specification to remove the objected hyperlink (Applicant’s Remarks, Pg. 7). Applicant’s arguments are not persuasive for the following reasons:
The clean and marked up specifications submitted on 11/24/2025 do not remove the prefix from the hyperlink in lines 3-5. This objection is therefore maintained.
Claim Objections
The objections to claims 1-2, 7, and 11 are withdrawn in view of Applicant’s amendments to the claims filed 11/24/2025 (Applicant’s Remarks, Pg. 7).
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
1. Claims 1-2 and 5-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
This rejection is newly recited and necessitated by claim amendment.
Any analysis of whether a particular claim is supported by the disclosure in an application requires a determination of whether that disclosure, when filed, contained sufficient information regarding the subject matter of the claims as to enable one skilled in the pertinent art to make and use the claimed invention. The standard for determining whether the specification meets the enablement requirement was cast in the Supreme Court decision of Minerals Separation Ltd. v. Hyde, 242 U.S. 261, 270 (1916) which postured the question: is the experimentation needed to practice the invention undue or unreasonable?. See also In re Wands, 858 F.2d 731, 737, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988). Accordingly, even though the statute does not use the term "undue experimentation," it has been interpreted to require that the claimed invention be enabled so that any person skilled in the art can make and use the invention without undue experimentation (MPEP § 2164.06).
The specification fails to enable the if a person of ordinary skill in the art would be faced with an undue burden of experimentation when trying to implement the invention based on the disclosure. In re Wands (858 F.2d 731 at 737, 8 USPQ2d 1400 at 1404 (Fed. Cir. 1988)) sets forth a non-exclusive list of factors by which this burden of experimentation may be judged to be due or undue; factors that are germane to the instant case include (a) the nature of the invention, (b) the quantity of experimentation needed to use the invention based on the content of the disclosure, (c) the amount of direction or guidance presented, (d) the existence of working examples, (e) the state of the prior art, (f) the level of predictability in the art, (g) the level of one of ordinary skill, and (h) the breadth of the claims.
Claim 1 recites the limitations “adjusting glucose concentration to maintain a target value of the glucose concentration” and “wherein the method is carried out without sampling and exclusively using on-line measured values of the process variables from mammalian cell cultivation”.
In considering the factors for the instant claims:
(a) The instant invention is drawn towards methods for adjusting the glucose concentration in any mammalian cell culture system.
(b) In order to practice the claimed invention, one of ordinary skill in the art must be able to perform a method for adjusting glucose concentration in mammalian cell culture, including determining values for process variables without sampling and exclusively using on-line measurements, determine the glucose concentration using a trained machine learning model, and adjust the glucose concentration to maintain a target value. For the reasons discussed below, the method of adjusting the glucose concentration to maintain a target value based on a machine learning model using exclusively on-line measured process variables constitutes undue experimentation.
(c) The Specification provides direction for steps (a) and (b) in claim 1. Specifically, the Specification (Pg. 28, Lines 3-5 and Pg. 58-59, Table 12) provides an example of 155 exemplary datasets that were obtained from cultivations in the ambr250 systems, and an overview of the on-line data. The Specification (Pg. 34-39 and Tables 5-8) also provides examples of three models – MLPRegressor, Random forest, and XGBoost – used to provide estimates for the process variables (VCD), glucose concentration, and lactate concentration.
Regarding step (c) in claim 1, the specification (Pg. 6-7, embodiment 3) discloses a method for adjusting the glucose concentration to a target value during the cultivation of a mammalian cell, comprising a step of (c) adding glucose until the target value is reached if the current glucose concentration as determined in b) is lower than the target value, and thereby adjusting the glucose concentration to a target value. As this is the only recitation of adjusting the glucose concentration in a mammalian cell culture system, the limitation is broadly recited without providing any steps or parameters for how the adjustment is occurs is any cell culture system in real-time.
(d) With respect to the examples in the Specification, the disclosure only provides a single example of the claimed determination using CHO cells that expressed a target molecule extracellularly in ambr250 bioreactors (Pg. 58, Line 10 – Pg. 60, Line 2; it is noted that the same dataset is used to optimize the machine learning model on Pg. 34-44). The cultivations were carried out using the fed-batch process. During the cell culture, the on-line parameters in Table 12 (Pg. 58-59) were measured and available for monitoring optimal cultivation conditions. The example further discloses that samples were taken during cultivation, and analyzed for metabolite concentrations (i.e., glucose and lactate) and product titers. The extracted samples were also used to provide information about live cell density, total cell density, viability, aggregation rate, and cell diameter, all off-line measured values. This example does not provide support for how the machine learning algorithm was used to calculate the glucose concentration in real-time (i.e., calculating current values and subsequently updating the model calculation), or that the determined glucose concentration was used to add/adjust glucose to maintain a target concentration in real-time during the cultivation. Therefore, it appears that the only example does not actually perform all claimed limitations of instant claim 1.
Additionally, the claims are broader in scope than the single example provided in the ambr250 bioreactors. The instant Specification (Pg. 28, Lines 7-9) discloses that other datasets, which have been generated with the same or a different cultivation system, can equally well be used for and in the method according to the invention. However, the instant Specification fails to provide any teaching of other datasets, other bioreactors, or other cell culture systems that can perform the claimed invention. Without any specifics as to how one would collect data and apply a (trained) machine learning model on any mammalian cell culture system (i.e., the claim is not limited to bioreactors) represents undue experimentation.
(e) and (f) It is well known in the art that one of the key components of cultivation medium is glucose, and the concentration of glucose during cell cultivation affects cell growth, viability, productivity, and protein product quality (see, for example, Comparison of multivariate data analysis techniques to improve glucose concentration prediction in mammalian cell cultivations by Raman spectroscopy. J Pharm Biomed Anal. 158: 269-279 (2018); previously cited). Therefore, methods to predict the glucose concentration during cultivation using on-line measurements will improve the protein product quality (Kozma et al., Pg. 270, Col. 1, Para. 1). Kozma et al. further discloses a method of predicting the glucose concentration using multiple linear regression (MLR) and principal component regression (PCR) techniques for comparison to the commonly used partial least squares regression (PLSR) and compares the predicted results to those measured from off-line analysis (Pg. 270, Col. 2, Para. 3 and Pg. 271, Col. 1, Para. 2). However, the predicted glucose concentrations are not used for real-time feedback, as disclosed in the instant claims.
Zhang et al. discloses a method of using real-time monitoring system that can be used to monitor and control glucose and lactate at high frequency without culture volume reduction (Advanced Process Monitoring and Feedback Control to Enhance Cell Culture Process Production and Robustness. Biotechnology and Engineering 112(12): 2495-2504 (2015); Pg. 2496, Col. 1, Para. 1). Their study demonstrated the capability and benefit of the real-time feed adjustment using bio-capacitance (BC) probe-based feedback control, which improves process robustness, provides consistency in productivity and product quality (Pg. 2500, Col. 1, Para. 1). However, Zhang et al. does not provide any predictive models for the glucose concentration, and only adjusts the glucose based on feedback from on-line monitoring.
Mehdizadeh et al. discloses a method for predicting the glucose, lactate and viable cell density (VCD) based on in-line Raman spectra (Generic Raman-Based Calibration Models Enabling Real-Time Monitoring of Cell Culture Bioreactors. Biotechnology Progress. 31(4): 1004-1013 (2015); Pg. 1006, Col. 2, Para. 4 and Pg. 1007, Col. 2, Para. 3). Mehdizadeh et al. further discloses that bioreactors were fed a concentrated nutrient solution on a semicontinuous basis at rates calculated based on working volume (Pg. 1006, Col. 2, Para. 2). Mehdizadeh et al. notes that the model predictions of glucose prediction are suitable for online-control of the rates that nutrients are fed into the culture, but does not disclose a method or protocol for doing so (Pg. 1012, Col. 1, Para. 1). Accordingly, similar to Kozma et al., Mehdizadeh et al. discloses a model for predicting glucose concentrations, but does not use the predicted concentrations for real-time control of the cell culture system, instead relying on semicontinuous feeding.
The above references to Kozma et al., Zhang et al., and Mehdizadeh et al. represent the state of the prior art and exemplify the challenges associated with monitoring the glucose concentration on-line and using models to predict the glucose concentration for real-time control of the cell culture system. What is clear from the prior art is that it is possible to monitor the glucose concentration using on-line parameters or predict the glucose concentration using various models, but not to use a predictive model as feedback for real-time control of glucose fed into the cell culture system.
The instant claims are not enabled as recited because they fail to include specific methods for adjusting the glucose concentration in real-time based on the predicted glucose concentration from a machine learning model for any mammalian cell culture system. The enablement of “adjusting glucose concentration to maintain a target value of the glucose concentration” is not realized because there are no parameters in the claims that include specific steps on how the predicted glucose concentration is used adjust the concentration in real time for any given mammalian system.
(g) The skill in the art of molecular and cellular biology is high, requiring specific cell culture techniques for individual cell types to monitor and optimize the cell culture process.
(h) The claims are broad because they are drawn to a method for adding glucose to any and all mammalian cell culture systems (e.g., bioreactors, microfluidic systems, shake flasks, etc.).
Because the disclosure does not provide a description of the adjustment of glucose using a machine learning algorithm in real-time, and because the methods of adjusting the glucose concentration based on model feedback in real-time are not readily available within the teachings of the prior art, a person of ordinary skill in the art who wished to practice the invention would have to perform additional experimentation to make and use the claimed invention. Specifically, that person would have to experiment to determine the method for adjustment of the glucose concentration in any and all mammalian cell culture systems, as well as the integrate real-time feedback for the model into the cell culture system. Whether or not the method of adjustment and the real-time control will be effective for all mammalian cell culture systems (e.g., bioreactors, microfluidic systems, shake flasks) cannot be predicted ahead of time, only by actually integrating the real-time control of the glucose concentration with the cell culture system and testing it. The vast number of methods to adjust the cell-culture in real-time, the vast number of mammalian cell culture systems, and the total absence of direction from the inventor regarding how to determine the adjustment of the glucose concentration in real-time results in that burden of experimentation being undue. The claims therefore fail to comply with the enablement requirement of 35 U.S.C. § 112(a).
2. Claims 1-2 and 5-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
This rejection is newly recited and necessitated by claim amendment.
Claim 1 recites the limitation “adjusting glucose concentration to maintain a target value of the glucose concentration” in step (c) of the claim. The specification (Pg. 6-7, embodiment 3) discloses a method for adjusting the glucose concentration to a target value during the cultivation of a mammalian cell, comprising a step of (c) adding glucose until the target value is reached if the current glucose concentration as determined in b) is lower than the target value, and thereby adjusting the glucose concentration to a target value. The specification (Pg. 58, Line 10 – Pg. 60, Line 2) further discloses an example in Ambr250 bioreactors. The example lists on-line measured variables that were used to monitor optimal cultivation conditions. However, the example does not show that any machine learning algorithm was used to adjust the glucose concentration. As such, the Specification broadly recites this limitation but does not provide any examples or indication that the inventor had possession of an invention to adjust the glucose levels in any and all mammalian cell culture systems. Accordingly, the disclosure is not commensurate with the scope of the claim. Claims 2 and 5-16 are also rejected due to their dependency on claim 1.
Claim 2 recites the limitation “wherein the process variables comprises following parameters: viable cell density, viable cell volume, glucose concentration, and lactate concentration in the cultivation medium”. Claim 1 recites the limitation “wherein the method is carried out without sampling and exclusively using on-line measured parameters”. Accordingly, the viable cell density, viable cell volume, glucose concentration, and lactate concentration in the cultivation medium need to be determined online and without sampling. The Specification (Pg. 58, Line 10 – Pg. 60, Line 2) discloses an example in Ambr250 bioreactors. The example discloses that samples were taken daily during cultivation. These were then analyzed for various concentrations of the metabolites and product titers. Additionally, Table 13 shows the following variables were measured off-line: live cell density, cell diameter (viable cell volume), lactate concentration, and glucose concentration. Therefore, the Specification discloses that these process variables are measured off-line, and not on-line as required by claim 1. Accordingly, the disclosure is not commensurate with the scope of the claim.
Claim Rejections - 35 USC § 112(b)
Withdrawn Rejections
The rejection of claims 1-16 under 35 U.S.C. 112(b) as being indefinite is withdrawn in view of Applicant’s amendments to the claims filed 11/24/2025 (Applicant’s Remarks, Pg. 8).
Newly Recited Rejections
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 1-2 and 5-16 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.
This rejection is newly recited and necessitated by claim amendment.
Claim 1 recites the limitation “determining a current glucose concentration in the cultivation medium using the current values of the process variables from (a) by means of a trained machine learning model for the mammalian cell cultivation, which is generated using a feature matrix comprising the process variables of (a)” in lines 12-15 of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. The Specification (see at least Pg. 34-38 and Pg. 38, Table 7) discloses that the trained machine learning algorithm can be a random forest algorithm, MLPRegressor, or XGBoost. The limitation recites steps of inputting a feature matrix of process variables and outputting a glucose concentration. However, it is unclear what parameters are necessary such that the trained model operates to provide the output predictive glucose concentration. Therefore, the metes and bounds of the invention are not clearly defined. Clarification of the metes and bounds of the claims through clearer claim language is respectfully requested. Claims 2 and 5-16 are also rejected due to their dependency from claim 1.
Claim Rejections - 35 USC § 101
Maintained Rejections
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 1-2 and 5-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Any newly recited portion is necessitated by claim amendment.
Step 1:
In the instant application, claims 1-2 and 5-16 are directed towards a method, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claim 1 recites a mathematical concept (i.e., using an algorithm to determine values) in “determining a current glucose concentration in the cultivation medium using the current values of the process variables from (a) by means of a trained machine learning model for the mammalian cell cultivation, which is generated using a feature matrix comprising the process variables of (a)”.
Claim 2 recites a mental process (i.e., a judgement of variables to include) in “wherein the process variables comprises following parameters: viable cell density, viable cell volume, glucose concentration, and lactate concentration in the cultivation medium”.
Claim 5 recites a mental process (i.e., an evaluation of the type of model) in “wherein the trained machine learning model is generated with random forest method”.
Claim 6 recites a mental process (i.e., an evaluation of the training data to be included in the model) in “wherein the trained machine learning model is generated with a training dataset comprising at least 10 cultivation runs”.
Claim 7 recites a mental process (i.e., an evaluation of the data to determine training and test datasets) in “dividing datasets available for the trained machine learning model randomly into a training dataset and a test dataset in a ratio of about 70:30 and 80:20”, a mathematical concept in “forming the trained machine learning model”, a mathematical concept in “determining first mean value and standard deviation for determining the process variables for the datasets from the training dataset; and determining second mean value and second standard deviation for determining the process variables for the test dataset”, and a mental process (i.e., evaluating the data to determine when to repeat the previous steps) in “repeating steps (a) to (c) until comparable mean values and standard deviations with respect to the test dataset and training dataset are achieved, wherein the ratio dividing the dataset in (a) is different with each repeat”.
Claim 8 recites a mental process (i.e., an evaluation of the data points in the data set) in “wherein the datasets contain the same number of data points for all of the process variables”.
Claim 9 recites a mental process (i.e., an evaluation of the data points in the data set) in “wherein each data point of the data points is collected at same times of the mammalian cell cultivation”.
Claim 10 recites a mathematical concept (i.e., using a formula to interpolate the data) in “wherein missing data points for glucose concentration, viable cell volume, viable cell density, and/or lactate concentration in the datasets are supplemented by interpolation”.
Claim 11 recites a mathematical concept (i.e., using a formula to generate missing data points) in “wherein the missing data points for the glucose concentration and/or the missing data points for the viable cell volume are obtained by third degree polynomial fit, the missing data points for the lactate concentration are obtained by univariate spline fit, and/or the missing data points for the viable cell density can be obtained through Peleg fit”.
Claim 12 recites a mental process (i.e., an evaluation of the data) in “wherein the each data point contains values of the process variables at least every 144 minutes”.
Claim 13 recites a mental process (i.e., an evaluation of the cells) in “wherein the mammalian cell is a CHO-K1 cell”.
Claim 14 recites a mental process (i.e., an evaluation of the cells) in “wherein the mammalian cell expresses and secretes an antibody”.
Claim 15 recites a mental process (i.e., an evaluation of data included in the training dataset) in “wherein the trained machine learning model is generated with a training dataset containing values of the process variables from complex IgG cultivation runs and standard IgG cultivation runs”.
Claim 16 recites a mental process (i.e., an evaluation of the cultivation volume) in “wherein a volume of the cultivation medium is 300 mL or less”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), 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)), and 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)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Specifically, the steps recited in claim 1 involve nothing more than determining a glucose concentration using a trained machine learning model. The step reciting “using a trained machine learning model” is, under the BRI, performed using mathematical operations. The instant Specification (see at least Pg. 34-38 and Pg. 38, Table 7) discloses that the trained machine learning algorithm can be a random forest algorithm. Therefore, the claimed steps are not further defined beyond something that reads on performing calculations using a computer as a tool. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following claims recite limitations that equate to additional elements:
Claim 1 recites “determining current values at least for following process variables ‘Elapsed time since fermentation start (Time)’, 'Temperature of cooling element (CHT.PV)', 'CO2 total in off-gas flow (ACOT.PV)', 'Feed 2 cumulative (FED2T.PV)', 'Fermenter weight (GEW.PV)', 'CO2 inflow cumulative (CO2T.PV)', 'CO2 concentration in off-gas flow (ACO.PV)', 'O2 concentration in off-gas flow (AO.PV)', 'N2 inflow (N2.PV)', 'Base addition cumulative (LGE.PV)', 'CO2 inflow (CO2.PV)', 'Feed 3 cumulative (FED3T.PV)', 'Oxygen utilization rate (OUR)', and 'Fermenter pH (PH.PV)', wherein the method is carried out without sampling and exclusively using on-line measured values of the process variables from the mammalian cell cultivation” and “adjusting glucose concentration to maintain a target value of the glucose concentration”.
Regarding the above cited limitations in claim 1 of (i) determining current values at least for following process variables ‘Elapsed time since fermentation start (Time)’, 'Temperature of cooling element (CHT.PV)' … 'Oxygen utilization rate (OUR)', and 'Fermenter pH (PH.PV)', wherein the method is carried out without sampling and exclusively using on-line measured values of the process variables from the mammalian cell cultivation; and (ii) adjusting glucose concentration to maintain a target value of the glucose concentration. Regarding limitation (i), this limitation equates to insignificant, extra-solution activity of mere data gathering because this limitation gathers data before the recited judicial exceptions of determining the current glucose concentration in the cultivation medium using the measured values by means of a trained machine learning model for the mammalian cell cultivation (see MPEP § 2106.04(d)). Regarding limitation (ii), this limitation equates to an extra-solution “apply it” step because the limitation used to physically adjust the glucose concentration without providing any details of how the adjustment is accomplished for all mammalian cell culture systems (see MPEP § 2106.05(f)). As such, claims 1-2 and 5-16 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitation in claim 1 of (i) determining current values at least for following process variables ‘Elapsed time since fermentation start (Time)’, 'Temperature of cooling element (CHT.PV)' … 'Oxygen utilization rate (OUR)', and 'Fermenter pH (PH.PV)', wherein the method is carried out without sampling and exclusively using on-line measured values of the process variables from the mammalian cell cultivation. This limitation is considered to be insignificant extra-solution activity of mere data gathering. This step is incidental to the primary process of determining the current glucose concentration by means of a trained machine learning model for the mammalian cell cultivation, wherein the measured process variables are merely inputs for the model (see MPEP § 2106.05(g)).
Regarding the above cited limitation in claim 1 of (ii) adjusting glucose concentration to maintain a target value of the glucose concentration. This limitation when viewed individually and in combination, is a WURC limitation as taught by Kozma et al. (Comparison of multivariate data analysis techniques to improve glucose concentration prediction in mammalian cell cultivations by Raman spectroscopy. J Pharm Biomed Anal. 158: 269-269 (2018); previously cited). Kozma et al. discloses that 2 M glucose stock solution was added to the bioreactors to adjust the glucose concentration to a target of 30 mM (limitation (ii)) (Pg. 271, Col. 1, Para. 2).
These 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 instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-2 and 5-16 are not patent eligible.
Response to Arguments under 35 U.S.C. 101
Applicant’s arguments filed 11/24/2025 have been fully considered but they are not persuasive.
1. Applicant argues that as amended, claim 1 recites a series of specific, technical steps that define a concrete manufacturing operation: (1) determining current values of the process variables; (2) determining the current glucose concentration in the cultivation medium by means of a trained machine learning model; and (3) adjusting the glucose concentration to maintain a specified target value. Taken together, these steps define a technical process for controlling a mammalian cell cultivation system, not an abstract idea. The claim is therefore directed to a statutory "process" under 35 U.S.C. § 101. Accordingly, claim 1 as amended herein is directed to a manufacturing process within the statutory category of a "process," not to an abstract idea. (Applicant’s Remarks, Pg. 9). Applicant’s arguments are not persuasive for the following reasons:
MPEP § 2106(III) recites:
Examiners should determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance with the following flowchart. The flowchart illustrates the steps of the subject matter eligibility analysis for products and processes that are to be used during examination for evaluating whether a claim is drawn to patent-eligible subject matter. It is recognized that under the controlling legal precedent there may be variations in the precise contours of the analysis for subject matter eligibility that will still achieve the same end result. The analysis set forth herein promotes examination efficiency and consistency across all technologies. As shown in the flowchart, Step 1 relates to the statutory categories and ensures that the first criterion is met by confirming that the claim falls within one of the four statutory categories of invention. See MPEP § 2106.03 for more information on Step 1. Step 2, which is the Supreme Court’s Alice/Mayo test, is a two-part test to identify claims that are directed to a judicial exception (Step 2A) and to then evaluate if additional elements of the claim provide an inventive concept (Step 2B) (also called "significantly more" than the recited judicial exception). See MPEP § 2106.04 for more information on Step 2A and MPEP § 2106.05 for more information on Step 2B.
Following the flow chart, under Step 1, Examiner agrees that claim 1 falls under the statutory category of a process. Since the claim is a process, further analysis under Step 2A is performed to determine if the claim is directed to a law of nature, natural phenomenon, or abstract idea. In the instant case, the limitation of “determining a current glucose concentration in the cultivation medium using the current values of the process variables from (a) by means of a trained machine learning model…” (step (b) in claim 1 / identified by Applicant as (2) above) recites an abstract idea. As described in Step 2A, Prong One above, step (b) recites a mathematical concept. The “trained machine learning model”, is under the BRI performed using mathematical operations using the computer as a tool, since the instant Specification discloses that the trained machine learning algorithm can be a random forest algorithm (see at least Pg. 34-38 and Pg. 38, Table 7). Therefore, claim 1 is a process under Step 1, and is directed to a judicial exception (mathematical concept) under Step 2A, Prong One. This argument is thus not persuasive.
2. Applicant argues that even if Examiner were to view the method as involving a mental process, the claimed steps cannot practically be performed in the human mind or on paper. The determination of glucose concentration requires the operation of a trained machine learning model that processes multiple, continuously updated process-sensor variables in real-time and issues control commands to a physical actuator (i.e., a glucose-feed pump). No human operator could mentally perform these calculations or execute the associated control logic (Applicant’s Remarks, Pg. 9). Applicant’s arguments are not persuasive for the following reasons:
MPEP § 2106.04(a)(2)(III)(C) recites:
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer") … Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
As indicated in Step 2A, Prong One above, step (b) of “determining a current glucose concentration... using a trained machine learning model…” recites a mathematical concept. The instant Specification (see at least Pg. 34-38 and Pg. 38, Table 7) discloses that the trained machine learning algorithm can be a random forest algorithm. As disclosed in MPEP § 2106.04(a)(2)(III)(C) above, a general computer can be used as a tool to perform a mental process. Therefore, the claimed steps are not further defined beyond something that reads on performing calculations using a computer as a tool. This argument is thus not persuasive.
3. Applicant argues that USPTO Memorandum dated August 4, 2025, which clarifies that "a claim does not recite a mental process when it contains limitation( s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)" on page 2, fifth paragraph. Claims as amended herein clearly fall within that category. The human mind is not equipped to execute the iterative, multi-dimensional computations of a trained machine learning model or to regulate a glucose feed actuator in real-time (Applicant’s Remarks, Pg. 9). Applicant’s arguments are not persuasive for the following reasons:
As indicated in the arguments directly above, the step of “determining a current glucose concentration... using a trained machine learning model…” merely uses the computer as a tool to execute the calculation. The step of “adjusting glucose concentration to maintain a target value of the glucose concentration” (regulating a glucose feed actuator in real-time, as indicated by Applicant) has been identified as an additional element in Step 2A, Prong Two above. Additional elements are not required to be practically be performed in the human mind (see MPEP § 2106.04). This argument is thus not persuasive.
4. Applicant argues that claim 1 as amended herein is not directed to a judicial exception. However, even assuming, arguendo, that the trained machine learning model involves mathematical operations, claim 1 as amended herein integrates those operations into a specific and practical technical and biological process. The trained machine learning model is expressly implemented within the context of a mammalian cell cultivation system, where the calculated output is used to directly control a physical action (i.e., adjusting of glucose concentration in the cultivation medium). This control action produces a real-world transformation; that is, it adjusts the glucose concentration in the cultivation medium and consequently modulates the metabolic flux of living cells within the cultivation medium. Accordingly, the claimed method does not merely collect or analyze data; it integrates the trained machine learning model within a physical process that changes the state of a tangible system. (Applicant’s Remarks, Pg. 10). Applicant’s arguments are not persuasive for the following reasons:
MPEP § 2106.05(c) recites:
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two and whether a claim recites significantly more in Step 2B is whether the claim effects a transformation or reduction of a particular article to a different state or thing … 3. The nature of the transformation in terms of the type or extent of change in state or thing. A transformation resulting in the transformed article having a different function or use, would likely provide significantly more, but a transformation resulting in the transformed article merely having a different location, would likely not provide significantly more (or integrate a judicial exception into a practical application). For example, a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diamond v. Diehr, 450 U.S. 175, 184, 209 USPQ 1, 21 (1981)), provides significantly more (or integrate a judicial exception into a practical application).
As indicated in Step 2A, Prong Two above, step (c) of “adjusting glucose concentration to maintain a target value of the glucose concentration” recites an extra-solution step “apply it” because the limitation is physically adjusting the glucose concentration, at a high level of generality, without providing any details about how this is accomplished (i.e., the steps or parameters involved) for all mammalian cell culture systems (see MPEP § 2106.05(f)). Regarding the real-world transformation to adjust the glucose concentration and thereby modulate the metabolic flux of living cells, the adjusted glucose concentration is merely feeding the cells, and therefore does not change the function or use of the cells (see MPEP § 2106.05(c)). The metabolic flux of the cells will be modulated with or without the added glucose. Therefore, the glucose adjustment does not change the state of the system, and this argument is not persuasive.
5. Applicant argues that the present invention employs an unconventional use of on-line process variables as input features. Such use of the on-line process variables to infer metabolite concentrations without any manual sampling or spectroscopy is not routine in the field. The present invention employs on-line values recorded during cultivation, without requiring manual sampling, thereby making the process more efficient and robust. This approach enables real-time analysis of measured parameters and their interrelationships using a trained machine learning model. Accordingly, the limitations of claims 1-16 as amended herein do not equate to well-understood, routine, and conventional (WURC) limitations, as Examiner asserts. Instead, the claimed limitations represent a technical improvement to a manufacturing process, specifically to the control and stability of mammalian cell cultivation systems. Therefore, the limitations of the present invention amount to "significantly more" than any alleged judicial exception. They constitute a specific, inventive technical solution to the longstanding problem of glucose control in biologics manufacturing (Applicant’s Remarks, Pg. 10-11). Applicant’s arguments are not persuasive for the following reasons:
MPEP § 2106.05(g) recites:
Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.
As described in Step 2B above, step (a) of determining the current values for the online process variables has been identified as an extra-solution activity of data gathering is mere data gathering, wherein the values for the current values for the online process variables are merely inputs for the model. This is similar to the pre-solution activity of obtaining information about credit card transactions, which is recited as part of the claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent, as described in MPEP § 2106.05(g) above. The fact that the process variables are all recorded on-line during cultivation does not change that the step (a) is identified as mere data gathering step.
Additionally, as described in Step 2B above, step (c) of adjusting the glucose concentration is a WURC limitation as taught by Kozma et al. Kozma et al. discloses that 2 M glucose stock solution was added to the bioreactors to adjust the glucose concentration to a target of 30 mM (Pg. 271, Col. 1, Para. 2). Because step (c) is recited at a high level of generality, and does not provide any specific steps or parameters for adjusting the glucose for any and all mammalian cell culture systems, Kozma et al. discloses step (c). Therefore, when viewed in combination, steps (a) and (c) do not amount to significantly more than the judicial exception, and are not considered an improvement in longstanding problem of glucose control in biologics manufacturing. This argument is thus not persuasive.
Claim Rejections - 35 USC § 103
The rejection of claims 1-4, 6, 12, and 14 under 35 U.S.C. 103 as being unpatentable over Kozma et al. in view of Charaniya et al. and Kornecki et al. is withdrawn in view of Applicant’s amendments to the claims filed 11/24/2025.
The rejection of claims 5 and 15 under 35 U.S.C. 103 as being unpatentable over Kozma et al. in view of Charaniya et al., Kornecki et al., and Hutter et al. is withdrawn in view of Applicant’s amendments to the claims filed 11/24/2025.
The rejection of claims 7-11 and 13 under 35 U.S.C. 103 as being unpatentable over Kozma et al. in view of Charaniya et al., Kornecki et al., and Schmitt et al. is withdrawn in view of Applicant’s amendments to the claims filed 11/24/2025.
The rejection of claim 16 under 35 U.S.C. 103 as being unpatentable over Kozma et al. in view of Charaniya et al., Kornecki et al., and Xu et al. is withdrawn in view of Applicant’s amendments to the claims filed 11/24/2025.
Conclusion
No claims allowed.
It is noted that claims 1-2 and 5-16 as currently recited are not enabled nor do they have adequate written description in the instant Specification, rendering a meaningful search of the art not possible at this moment. Insofar as prior art is not applied to claims 1-2 and 5-16 in the instant rejection, prior art will be re-assessed upon any amendment.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687