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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 12/2/2025 has been entered and considered. Rejections and/or objections not reiterated from the previous office action mailed 9/9/2025 are hereby withdrawn. The following rejections and/or objections are either newly applied or are reiterated and are the only rejections and/or objections presently applied to the instant application.
Status of the Claims
Claims 1-4, 6, 8-10, 12, 14-15, 18-24, and 40-43 are pending and under consideration in this action. Claims 40-43 are newly added. Claims 5, 7, 11, 13, 16-17, and 25-39 were previously canceled.
Priority
The instant application is 371 of PCT/US20/26386, filed 04/02/2020, which claims priority to U.S. Provisional Application number 62/828,696, filed 04/03/2019. The claim for domestic benefit for claims 1-4, 6, 8-10, 12, 14-15, 18-24, and 40-43 is acknowledged. As such, the effective filing date of claims 1-4, 6, 8-10, 12, 14-15, 18-24, and 40-43 is 04/03/2019.
Withdrawn Rejections
35 U.S.C. 101
The rejection of claims 1-4, 6, 8-10, 12, 14-15, and 18-24 under 35 U.S.C. 101 as being directed to an abstract idea without significantly more is withdrawn in view of Applicant’s amendments to the claims filed 12/2/2025 and Applicant’s arguments were found persuasive (Applicant’s Remarks, Pg. 7-10). Claims 1-4, 6, 8-10, 12, 14-15, 18-24 and newly added 40-43 were analyzed under 35 U.S.C. 101 and it was found that, as amended, the limitation of “wherein the machine learning system is configured to normalize and categorize the first data input and the second data input to generate a plurality of feature vectors and to determine statistically significant associations between the first data input and the second data input to thereby generate an optimized cell culture protocol tailored to the cells to be cultured” in independent claims 1 and 12 recites a judicial exception (mathematical concept) under Step 2A, Prong One, in the normalizing, categorizing, and statistically analyzing the input data. However, the limitations of “receive data associated with desired cells to be cultured as a first data”, “communicate with the one or more databases to retrieve cell culture protocol data relevant to the desired cells as a second data input”, “analyze the first data input and the second data input using a machine learning system comprising a deep learning neural network comprising an input layer, a plurality of hidden layers, and an output later”, “receive, as a third data input, real-time sensor feedback from the one or more sensors during cell culture”, and “dynamically adjust the cell culture protocol during cell culture based on the sensor feedback to thereby maintain optimized specific cell culture conditions”, when viewed in combination, amount to significantly more than the judicial exception. In combination, the limitations are not well-understood, routine, and conventional, and provide a dynamic feedback loop for tailored cell culture protocol optimization (Applicant’s Remarks Pg. 9-10)
Claim Rejections - 35 USC § 112(b)
Withdrawn Rejections
The rejection of claims 6, 8, and 14 under 35 U.S.C. 112(b) as being indefinite is withdrawn in view of Applicant’s amendments to the claims filed 12/2/2025 (Applicant’s Remarks, Pg. 7).
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 18-23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
This rejection is newly recited and necessitated by claim amendment.
Claim 18 recites the limitation “reporting the determined cell culture protocol” in lines 1-2 of the claim. There is insufficient antecedent basis for this limitation in the claim, since there is no prior mention of this phrase in claim 12, to which this claim depends. This rejection can be overcome by amendment of claim 18 to recite “reporting the optimized cell culture protocol”. Claims 19-23 is also rejected due to their dependence from claim 18.
Claim Rejections - 35 USC § 103
Withdrawn Rejections
The rejection of claims 1, 3-4, 6, 8-10, 12, 14-15, 18, and 20-23 under 35 U.S.C. 103 as being unpatentable over Murthy, Amirkia and He et al. is withdrawn in view of Applicant’s amendments to the claims filed 12/2/2025 (Applicant’s Remarks, Pg. 10-14).
The rejection of claim 2 under 35 U.S.C. 103 as being unpatentable over Murthy, Amirkia, He et al., and Konagaya et al. is withdrawn in view of Applicant’s amendments to the claims filed 12/2/2025 (Applicant’s Remarks, Pg. 10-14).
The rejection of claims 19 and 24 under 35 U.S.C. 103 as being unpatentable over Murthy, Amirkia, He et al., and Franscini et al. is withdrawn in view of Applicant’s amendments to the claims filed 12/2/2025 (Applicant’s Remarks, Pg. 10-14).
Newly Recited Rejections
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1, 3-4, 6, 8-10, 12, 14-15, 18, and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Murthy (WIPO Application, WO 2018/005521 A2; published 01/04/2018; provided in the IDS dated 10/06/2021; previously cited) in view of Amirkia (Bioinformation 8(5), 237-8 (2012); published 03/17/2012; previously cited), Mehrotra et al. (Efficiency of neural networks for prediction of in vitro culture conditions and inoculum properties for optimum productivity. Plant Cell Tiss Organ Cult. 95: 29-35 (2008); published 6/26/2008; newly cited), and Sewsynker-Sukai et al. (Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnology & Biotechnological Equipment. 31(2): 221-235 (2017); published 12/27/2016; newly cited).
This rejection is newly recited and necessitated by claim amendment.
Regarding claim 1, Murthy teaches that the cell culture chamber includes varies technical features that allows for automation, dramatically reducing user intervention in the process (Pg. 2, Lines 13-16). During process integration and optimization, the process decision variables, including temperature, pressure, flow-rate and channel dimensions, are varied to achieve the desired trade-off between yield, purity and throughput (i.e., a system for dynamically optimizing a cell culture protocol) (Pg. 25, Lines 27-29). Murthy further teaches that the computer of the system is configured to communicate across a network (i.e., a cell culture apparatus configured to communicate) (Pg. 28, Lines 1-3). Murthy further teaches that the cell culture system includes one or more cell culture chambers and a central processing unit comprising memory containing instructions executable by the central processing unit to cause the system to receive data (i.e., the cell culture apparatus comprising a controller) (Pg. 5, Lines 19-22). Murthy further teaches that the cell culture chamber may further comprise a central processing unit communicatively coupled to the one or more sensors and configured to adjust an operating state of the one or more pumps as a function of the one or more parameters measured (i.e., one or more sensors communicatively coupled to the controller) (Pg. 5, Lines 26-29). Murthy further teaches that the sensors are operably coupled to the cell culture chamber to allow the sensors to measure one or more parameters, such as pH, glucose concentration, etc. (i.e., one or more sensors configured to measure one or more cell culture conditions) (Pg. 4, Lines 23-26). Murthy further teaches that the system includes a computer a video display unit for any of the I/O, as well as a keyboard, mouse, touchscreen, microphone, etc. for input or output (i.e., an interface with which one or more users can interact, via associated computing devices, for dynamically optimizing a cell culture protocol and receive first data as input from the one or more users) (Pg. 28, Lines 1-18). Murthy further teaches that the elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will include, or be operatively coupled to receive data from or transfer data to, or both, one or more non-transitory mass storage devices for storing data (i.e., the controller comprises a hardware processor coupled to memory containing instructions executable by the processor) (Pg. 25, Lines 19-32). Murthy further teaches that the system receives first input data comprising the size of the cell culture chamber, receives second input data comprising a first concentration of a first cell type and a second centration of a second cell type in one or more fluids that will be introduced into the cell culture chamber (i.e., receive data associated with the desired cells to be cultured as a first data) (Pg. 5, Lines 22-25). Murthy further teaches that during process integration and optimization, the process decision variables, including temperature, pressure, flow-rate and channel dimensions, are varied to achieve the desired trade-off between yield, purity and throughput (i.e., wherein the optimized cell culture protocol comprises one or more specific cell culture conditions) (Pg. 24, Lines 27-30). Murthy further teaches that the cell culture chamber may further comprise a central processing unit communicatively coupled to the one or more sensors and configured to adjust an operating state of the one or more pumps as a function of the one or more parameters measured (i.e., based on the sensor feedback). In an embodiment in which a flow generating mechanism is employed rather than pumps, such as an electro hydrodynamics mechanism, the central processing unit may change an operating state of the flow generating mechanism to adjust a rate of flow of the first cell product as a function of the one or more parameters (i.e., receive, as a third data input, real-time sensor feedback from the one or more sensors during cell culture) (Pg. 4, Lines 26-33). Murthy further teaches that the start-up dynamics are analyzed using both simulation and experimentation, the results of which are used to perform a start-up optimization by implementation of real-time feedback control (i.e., dynamically adjust the cell culture protocol during cell culture to maintain optimized specific cell culture conditions) (Pg. 25, Lines 2-4).
Regarding claim 3, Murthy teaches that the methods can be performed using software, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions can also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations (e.g., imaging apparatus in one room and host workstation in another, or in separate buildings, for example, with wireless or wired connections) (i.e., wherein the controller is distributed) (Pg. 25, Lines 13-18).
Regarding claim 4, Murthy teaches that the methods, devices, and systems of the cell culture apparatus can be scaled up to provide a large number of cell-based immunotherapeutic products, and can be operated either for a single subject (i.e., a single-use cell culture apparatus) or for several subjects in parallel (Pg. 8, Lines 17-20). Murthy further teaches that in some embodiments, all components of the system are disposable (i.e., single-use cell culture apparatus) (Pg. 5, Lines 6-8).
Regarding claim 6, Murthy teaches that the cell culture chamber includes one or more sensors operably coupled to the cell culture chamber (Pg. 14, Lines 19-20). Murthy further teaches that the methods, devices, and systems can be scaled up to provide a large number of cell-based immunotherapeutic products, and can be operated for either a single subject (i.e., single-use) or for several subjects in parallel (Pg. 8, Lines 17-20). Murthy further teaches that in some embodiments, all components of the system are disposable (i.e., wherein the one or more sensors are single-use sensors) (Pg. 5, Lines 6-8).
Regarding claim 8, Murthy teaches that the cell culture chamber includes one or more sensors operably coupled to the cell culture chamber in a manner that allows the sensors to measure one or more parameters such as pH, dissolved oxygen, total biomass, cell diameter, glucose concentration, lactate concentration, and cell metabolite concentration with the cell culture chamber (i.e., the feedback is associated with a cell culture condition comprising at least one of pH, glucose concentration, lactate concentration, dissolved oxygen, total biomass, cell diameter, temperature, cell type, media type, and fluid flow rate) (Pg. 4, Lines 23-26).
Regarding claim 12, Murthy teaches the limitations of providing a cell culture apparatus configured to communicate; wherein the cell culture apparatus comprises a controller; one or more sensors communicatively coupled to the controller and configured to measure one or more cell culture conditions; an interface with which one or more users can interact, via associated computing devices, for dynamically optimizing a cell culture protocol; wherein the controller comprises a hardware processor coupled to memory containing instructions executable by the processor; receiving, from the one or more users, data associated with desired cells to be cultured as a first input; wherein the optimized cell culture protocol comprises one or more specific cell culture conditions; receiving, as a third data input, real-time sensor feedback from the one or more sensors during cell culture; and dynamically adjusting the cell culture protocol during cell culture based on the sensor feedback to thereby maintain optimized specific cell culture conditions as described for claim 1 above.
Regarding claim 14, Murthy teaches that the cell culture chamber includes one or more sensors operably coupled to the cell culture chamber in a manner that allows the sensors to measure one or more parameters such as pH, dissolved oxygen, total biomass, cell diameter, glucose concentration, lactate concentration, and cell metabolite concentration within the cell culture chamber (i.e., wherein the feedback is associated with a cell culture condition comprising at least one of pH, glucose concentration, lactate concentration, dissolved oxygen, total biomass, cell diameter, temperature, cell type, media type, and fluid flow rate) (Pg. 4, Lines 23-26).
Regarding claims 18 and 22, Murthy teaches that to provide interaction with a user, the subject matter is implemented on a computer having an I/O device for displaying information to the user and an input or output device such as a keyboard and a pointing device, by which the user can provide input to the computer. Feedback provided to the user can also be any form of sensory feedback (i.e., reporting the determined cell culture protocol and providing monitoring information to the user) (Pg. 25, Lines 33-34 – Pg. 26, Lines 1-6).
Regarding claim 20, Murthy teaches that the feedback provided to the user can be any form of sensory feedback, including visual, auditory or tactile (i.e., the alert comprises a voice alert) (Pg. 26, Lines 3-6).
Regarding claim 21, Murthy teaches that the cell culture chamber includes one or more sensors operably coupled to the cell culture chamber in a manner that allows the sensors to measure one or more parameters such as pH, dissolved oxygen, total biomass, cell diameter, glucose concentration, lactate concentration, or cell metabolite concentration within the cell culture chamber (i.e., a level comprises a pH level, dissolved oxygen level, total biomass level, cell diameter level, or temperature level) (Pg. 4, Lines 23-26).
Regarding claim 23, Murthy teaches that the cell culture chamber can also include one or more sensors operably connected to the cell culture chamber in a manner that allows the sensors to measure one or more parameters, such as pH, dissolved oxygen, total biomass, cell diameter, glucose concentration, lactate concentration, and cell metabolite concentration within the cell culture chamber (Pg. 4, Lines 23-29). Murthy further teaches that the system includes or is operably coupled to one or more control systems for monitoring and controlling various parameters, such as temperature (i.e., monitoring information comprises profiles of pH, dissolved oxygen, total biomass cell diameter, and temperature) (Pg. 24, Lines 4-6).
Murthy does not teach with one or more databases containing cell culture protocol data; communicate with the one or more databases to retrieve cell culture protocol data relevant to the desired cells as a second data input; and analyze the first data input and the second data input using a machine learning system comprising a deep learning neural network comprising an input layer, a plurality of hidden layers, and an output layer, wherein the machine learning system is configured to normalize and categorize the first data input and the second data input to generate a plurality of feature vectors and to determine statistically significant associations between the first data input and the second data input to thereby generate an optimized cell culture protocol tailored to the cells to be cultured.
Regarding claims 1 and 12, Amirkia teaches the creation of a practical, user-friendly database containing cell-lines, plasmids, vectors, selection agents, concentrations, and media (Pg. 237, Abstract). Amirkia further teaches that the electronic, web-based version of the database is written in C# and can be accessed at http://cell-lines.toku-e.com/ (i.e., with the one or more databases to receive cell culture protocol data and communicate with the one or more databases to retrieve cell culture protocol data relevant to the desired cells as a second data input) (Pg. 238, Col. 1, Para. 1).
Regarding claims 1 and 12, Mehrotra et al. teaches an artificial neural network based computational scheming of physical, chemical, and biological parameters at flask level for mass multiplication of plants through micropropagation using bioreactors of larger volumes. The optimal culture environment at small scale was predicted by using a neural network approach in terms of pH and volume of growth medium per culture flask, incubation temperature and month of inoculation, along with the inoculation properties of size, fresh weight, and number of explant per flask (i.e., analyze the first data input and the second data input using a machine learning system comprising a deep learning neural network to thereby generate an optimized cell culture protocol tailed to the cells to be cultured) (Pg. 29, Abstract). Mehrotra et al. further teaches that the network contains an input layer (seven input elements), a single hidden layer (seven nodes), and one output layer (with a single output unit) (i.e., comprising an input layer and an output layer) (Pg. 31, Col. 1, Para. 1). Mehrotra et al. further teaches that that initialization of the weights and bias was random (i.e., wherein the machine learning system is configured to normalize the first data input and the second data input) (Pg. 31, Col. 1, Para. 1). Mehrotra et al. further teaches that the feed-forward back propagation type network creates an input vector containing seven input elements (Pg. 31, Col. 1, Para. 1). There are 14 sets of training data and 33 sets of testing data (i.e., each set of data as an input vector; categorize the first data input and the second data input to generate a plurality of feature vectors) (Pg. 31, Col. 2, Para. 3).
Regarding claims 1 and 12, Sewsynker-Sukai et al. teaches the application of artificial neural networks (ANNs) for the modeling and enhancement of bioprocess research (Abstract). Sewsynker-Sukai et al. further teaches that ANNs are entirely data-based with no previous knowledge of the events that govern the process. They consist of an input layer, one or more hidden layers and an output layer (i.e., comprising an input layer, a plurality of hidden layers and an output layer) (Pg. 22, Col. 2, Para. 2 and Pg. 223, Fig. 1). Sewsynker-Sukai et al. further teaches that the neuron adds the weighed inputs and forwards the outcome to a transfer function to produce an output. The weights are referred to as the attachment strength linking the neurons. As a result of some input signals being more significant compared to others, the utilization of weights as equivalent to the significance of each input signal provides a well-organized process to create an ideal output. The values of weights are adapted during the training phase of the network (i.e., determine statistically significant associations between the first data input and the second data input) (Pg. 224, Col. 1, Para. 5 – Col. 2, Para. 1)
Regarding claims 9, Amirkia teaches that the datasets contained in the database allow for the user to confirm the use of appropriate selection agents and concentrations. At the same time, users can switch to an entirely new set of media and/or selection agents from the displayed alternatives encompassed therein. Cultivation conditions can also be adjusted based on the alternatives displayed in the database (i.e., the one or more databases is a database comprising one or more cell culture protocols previously developed by the system) (Pg. 238, Col. 2, Para. 2).
Regarding claims 10, Amirkia teaches that the database can be readily accessed at http://cell-lines.toku-e.com/ (Pg. 238, Col. 1, Para. 1) and has received thousands of visitors in the first few months (i.e., a publicly available database) (Pg. 238, Col. 2, Para. 3). Amirkia further teaches that the database consists of over 3,900 cell lines (human and mammalian) and 1,900 plasmids/vectors collected from 2,700 pieces of published literature. The database also includes details related to experimental conditions such as cultivation temperature and time (i.e., comprising one or more cell culture protocols) (Pg. 238, Col. 1, Para. 2).
Regarding claim 15, Amirkia teaches the limitation of wherein the one or more databases is a database comprising one or more cell culture protocols previously developed by a system for monitoring and controlling cell culture and a publicly available database comprising one or more cell culture protocols as described for claims 9 and 10 above.
Therefore, regarding claims 1, 3-4, 6, 8-10, 12, 14-15, 18, and 20-23, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify system and method of determining cell culture protocol of Murthy with the teachings of Amirkia because the database not only seeks to allow users to confirm their experimental protocols, but also seeks to solve some of the uncertainties in cell culture through logical presentation of cell culture methods (Amirkia, Pg. 238, Col. 2, Para. 2). One of ordinary skill in the art would be able to combine the teachings of Murthy with Amirkia with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards methods for determining appropriate cell culture protocols.
It would also have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify system and method of determining cell culture protocol of Murthy with the neural networks for predicting culture conditions of Mehrotra et al. and Sewsynker-Sukai et al. because the approach described by Mehrotra et al., a neural network approach for forecasting of parameters influencing the growth and productivity of in vitro cultured plants at small culture flask level, is a prerequisite for the up-scaling of cultures in bioreactors of larger volumes (Mehrotra et al., Pg. 32, Col. 1, Para. 3). Additionally, Sewsynker-Sukai et al. discloses that the use of an artificial neural network model for real-time monitoring and control of bioreactors will contribute immensely to the development of a viable biofuel production system, providing motivation for the combination with Murthy. One of ordinary skill in the art would be able to combine the teachings of Murthy with Mehrotra et al. and Sewsynker-Sukai et al. with reasonable expectation of success due to the same nature of the problem to be solved, since all three are drawn towards methods for determining appropriate cell culture protocols. Therefore, regarding claims 1, 3-4, 6, 8-10, 12, 14-15, 18, and 20-23, the instant invention is prima facie obvious (MPEP § 2142).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Murthy in view of Amirkia, Mehrotra et al., and Sewsynker-Sukai et al. as applied to claims 1, 3-4, 6, 8-10, 12, 14-15, 18, and 20-23 above, and further in view of Konagaya et al. (Sci. Rep. 5, 16647, 1-9 (2015); published 11/17/2015; previously cited).
This rejection is newly recited and necessitated by claim amendment.
Murthy in view of Amirkia, Mehrotra et al., and Sewsynker-Sukai et al., as applied to claims 1, 3-4, 6, 8-10, 12, 14-15, 18, and 20-23 above, does not teach wherein the controller is integrated.
Regarding claim 2, Konagaya et al. teaches that the automated cell culture system is composed of a 6-axis robot arm, a CO2 incubator, a refrigerator, a heater, a centrifuge, a phase-contrast microscope, a trash box, a storage area for dishes, pipet tips, and centrifuge tubes (Pg. 5, Para. 8). Konagaya et al. further teaches that the motion and condition of all devices and units are controlled by the PC embedded in the automated culture system (i.e., wherein the controller is integrated) (Pg. 7, Para. 1).
Therefore, regarding claim 2, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify system for monitoring and controlling cell culture of Murthy in view of Amirkia, Mehrotra et al., and Sewsynker-Sukai et al. with the teachings of Konagaya et al. because automated cell culture systems enable the large-scale production of cells and improve the reproducibility of cell cultures (Konagaya et al., Abstract). One of ordinary skill in the art would be able to combine the teachings of Murthy in view of Amirkia, Mehrotra et al., and Sewsynker-Sukai et al. with Konagaya et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for automating and monitoring cell culture. Therefore, regarding claim 2, the instant invention is prima facie obvious (MPEP § 2142).
Claims 19, 24, and 40-43 are rejected under 35 U.S.C. 103 as being unpatentable over Murthy in view of Amirkia, Mehrotra et al., and Sewsynker-Sukai et al. as applied to claims 1, 3-4, 6, 8-10, 12, 14-15, 18, and 20-23 above, and further in view of Franscini et al. (J. Lab Autom. 6(3), 204-13 (2011); published 05/16/2011; previously cited).
This rejection is newly recited and necessitated by claim amendment.
Murthy in view of Amirkia, Mehrotra et al., and Sewsynker-Sukai et al., as applied to claims 1, 3-4, 6, 8-10, 12, 14-15, 18, and 20-23 above, does not teach wherein reporting comprises providing an alert when a level falls outside specific ranges; wherein determining the cell culture protocol further comprises deciding to terminate the culture process, to stop using further reagents, to alert the user, or to shut down the system; wherein the receive at least one set level of a user-defined parameter, wherein the specific cell culture conditions are optimized to maintain the set level of the user defined parameter; and wherein the user-defined parameter comprises at least one of pH, turbidity, glucose concentration, lactate concentration, a measure of cell health, a measure of cell identity, or a combination thereof.
Regarding claim 19, Franscini et al. teaches that an automated cell culture platform was developed based on the liquid-handling robot Freedom EVO 150 (Tecan) with added modules suitable for cell culture (Pg. 205, Col. 2, Para. 5). Franscini et al. further teaches that Brightfield analysis enables the calculation of growth rates, which can be used for quality control as (cell-specific) untypically low growth rates indicate alterations in the cellular metabolism. In this case, the system will issue an automated warning (i.e., providing an alert) (Pg. 211, Col. 2, Para. 4). Franscini et al. also teaches that an automated warning can be issued if the marker profile does not meet the set requirements (i.e., when a level falls outside specified ranges) (Pg. 212, Col. 2, Para. 1).
Regarding claim 24, Franscini et al. teaches that an automated warning can be issued by the system so that the user can decide whether to continue or to stop the culture (i.e., deciding to terminate the cell culture process, or to alert the user) (Pg. 211, Col. 2, Para. 4).
Regarding claims 40 and 42, Franscini et al. teaches that the pipetting software Freedom EVOware groups pipetting commands and device commands into scripts and allows creation of user- and cell-type specific experimental programs (i.e., wherein the system is configured to receive at least one set level of a user-defined parameter, wherein the specific cell culture conditions are optimized to maintain the set level of the user defined parameter and wherein the method further comprises receiving a set level of a user-defined parameter, wherein dynamically adjusting the cell culture protocol comprises optimizing the specific cell culture conditions to maintain the set level of the user-defined parameter) (Pg. 206, Col. 1, Para. 5 – Col. 2, Para. 1).
Regarding claims 41 and 43, Franscini et al. teaches that using the Cellavista tool in an automated platform not only allows the user to determine how confluent cells are, but also to accurately estimate total cell numbers if the cell-type specific correlation between cell numbers per area and confluence has been previously entered into the system. Importantly, no interference with the cells is necessary as the measurement can be done in the Robo-Flask by simply moving it from the incubator into the Cellavista analyzer using the robotic arm. If performed regularly during the culture period, growth rates can be calculated from the saved data, reflecting the real proliferation of the cells, which is an important quality control aspect (i.e., wherein the user-defined parameter comprises at least one of a measure of cell health) (Pg. 211, Col. 2, Para. 2).
Therefore, regarding claims 19, 24, and 40-43, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of determining a cell culture protocol of Murthy in view, Mehrotra et al., and Sewsynker-Sukai et al. with the teachings of Franscini et al. because automated systems provide safe, reproducible, effective, and affordable cell-based products that are able to meet good manufacturing practice (GMP) requirements (Franscini et al., Pg. 209, Col. 2, Para. 2). One of ordinary skill in the art would be able to combine the teachings of Murthy in view of Amirkia, Mehrotra et al., and Sewsynker-Sukai et al. with Franscini et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for automating cell culture. Therefore, regarding claims 19, 24, and 40-43, the instant invention is prima facie obvious (MPEP § 2142).
Response to Arguments under 35 U.S.C. 103
Applicant’s arguments filed 12/2/2025 have been fully considered but they are not persuasive.
Applicant argues that that none of Murthy, Amirkia, Qiubao, Konagaya, and Franscini, alone or in any proposed combination, disclose the system for dynamically optimizing a cell culture protocol of amended claim 1, or the method for dynamically optimizing a cell culture protocol of amended claim 12. (Applicant’s Remarks, Pg. 10-13). Applicant’s arguments are not persuasive for the following reasons:
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981 ); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Murthy discloses a cell culture system that is connected to a network (Murthy, Abstract and Pg. 26, Lines 7-15), while Amirkia discloses a database containing cell culture data, which is accessible online (Amirkia, Abstract and Pg. 238, Col. 1, Para. 1). Newly recited Mehrotra et al., and Sewsynker-Sukai et al. teach the use of artificial neural networks for prediction of optimized cell culture conditions (Mehrotra et al., Abstract and Sewsynker-Sukai et al., Abstract). When the teachings of Murthy, Amirkia, Mehrotra et al., and Sewsynker-Sukai et al. are viewed together, they demonstrate a cell culture system that is operatively connected to a database to receive cell culture protocol data, which is dynamically optimized using a machine learning model. This argument is thus not persuasive.
Applicant's arguments regarding the combination of Murthy, Amirkia, Qiubao, Konagaya, or Franscini to teach or suggest a system or method for dynamically optimizing a cell culture protocol as disclosed in amended claims 1 and 12 have been considered but they are not persuasive in view of the new grounds of rejection that relies on a new combination of references as necessitated by claim amendment (Applicant’s Remarks, Pg. 13-14).
Conclusion
No claims allowed.
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/D.P.S./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687