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 .
Applicant's response filed 3/2/2026 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1, 3, and 7-9 pending and examined on the merits.
Claims 2 and 4-6 canceled.
Priority
The instant application filed on 3/9/2022 is a CON of International Application No. PCT/JP2020/018809 filed on 5/11/2020, and claims the benefit of foreign priority to Application No. JP2019-173365 filed on 9/24/2019. Thus, the effective filing date of the claims is 9/24/2019.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Claim Objections
The objection to claim 6 withdrawn in view of Applicant's claim amendments filed on 3/2/2026.
Claims 1 and 8-9 objected to because of the following informalities: lines 19-20, 18-19, and 18-19, respectively, "derive, [], derived process conditions" should read "derive, [], process conditions". Appropriate correction is required.
Withdrawn Rejections
35 USC § 112(b)
The rejection of claims 1-9 under 35 USC 112(b) in the Office Action filed 9/29/2025 withdrawn in view of Applicant's claim amendments filed on 3/2/2026.
35 USC § 112(d)
The rejection of claim 6-7 under 35 USC 112(d) withdrawn in view of Applicant's claim amendments filed on 3/2/2026.
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 1, 3, and 7-9 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, 8, and 9 recites "acquire, from the cell culture device, a culture state of the cells in a culture medium contained in the cell culture device, the culture state including at least one of the number of the cells". There is insufficient antecedent basis for “the cells in a culture medium” or "the number of the cells". To further prosecution, the limitation is interpreted as “acquire, from the cell culture device, a culture state of cells in a culture medium contained in the cell culture device, the culture state including at least one of a number of the cells".
Claims 3 and 7 rejected as depending from indefinite claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, and 7-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1, 8, and 9: “estimate, using a first trained model, an estimated quality of an antibody produced from cells and an estimated quality of the cells on the basis of the acquired culture state” provides an evaluation (estimating an antibody or cell quality involves evaluation of data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea (based on interpretation above).
“search, using a predetermined search algorithm, for a target culture state for which the estimated quality of the antibody and the estimated quality of the cells exceeds a predetermined evaluation threshold” provides a comparison (searching using a predetermined threshold involves comparison of estimated qualities to the threshold) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea (based on interpretation above).
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 1, 3, and 7-9 recite performing some aspects of the analysis on “An information processing apparatus connected to a cell culture device, the information processing device comprising at least one processor and a display”, “An information processing method executed by a computer”, and “A non-transitory computer-readable storage medium storing an information processing program for causing a computer to execute”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1, 3, and 7-9 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claims 1, 8, and 9: “An information processing apparatus connected to a cell culture device, the information processing device comprising at least one processor and a display”, “An information processing method executed by a computer”, and “A non-transitory computer-readable storage medium storing an information processing program for causing a computer to execute” provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
“acquire, from the cell culture device, a culture state of the cells in a culture medium contained in the cell culture device, the culture state including at least one of the number of the cells, a pH of the culture medium, a concentration of a dissolved gas in the culture medium, or a gas transfer capacity coefficient of the culture medium” provides insignificant extra-solution activities (acquiring data from a cell culture device is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“the first trained model being trained on learning data pairing a culture state at a first time with a quality of the antibody and a quality of the cells at a second time later than the first time, the quality of the antibody including at least one of a concentration of the antibody, an aggregate amount of the antibody, a decomposition product amount of the antibody, or an immature sugar chain amount, and the quality of the cells including at least one of a cell survival rate or a cell viability” and “derive, using a second trained model that has been trained on learning data pairing a culture state with process conditions, derived process conditions by which the cell culture device is to be operated such that the acquired culture state becomes the target culture state” provides insignificant extra-solution activities (training and using a model is a pre-solution activity involving data gathering and manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“output the derived process conditions at the display” provides insignificant extra-solution activities (outputting and displaying data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
The steps for acquiring, outputting, and displaying data, and training and using a model are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1, 3, and 7-9 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “An information processing apparatus connected to a cell culture device, the information processing device comprising at least one processor and a display”, “An information processing method executed by a computer”, nor “A non-transitory computer-readable storage medium storing an information processing program for causing a computer to execute” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for acquiring, outputting, and displaying data, and training and using a model are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional as evidenced by: Kempner et al. ("A review of cell culture automation." JALA: Journal of the Association for Laboratory Automation 7.2 (2002): 56-62) Page 1 col 1 paragraph 3 "A small number of companies manufacture fully automated cell culture systems. In order to be fully automated, their systems must provide an environment for the cells to grow as well as the capability to monitor cell growth without human interaction. The automated cell culture systems can operate unattended over a period of days or weeks and allow for evaluation of pH, nutrient or waste concentration, as well as cell concentration and viability" and Naugler et al. ("Automation and artificial intelligence in the clinical laboratory." Critical reviews in clinical laboratory sciences 56.2 (2019): 98-110) Page 4 col 2 last paragraph "Although TLA systems are continuously evolving to deliver improved hardware and software components, they have only recently moved to being modular. In the past, clinical microbiology had to acquire a TLA system “in total”, but manufacturers are moving to selling the pre-analytical automated specimen handling/processing instruments separately from other components, which may make automation more feasible for more laboratories. These large instruments automate and integrate front-end processes such as inoculation and streaking of culture plates along with subsequent movement of culture plates to incubators that are coupled to high-resolution cameras for imaging and selection of isolates for analysis work-up", and goes on to list cell culturing as an obvious use case, as well as applying machine learning systems to the automatically generated data on page 6 col 2 paragraph 2 "Indeed, the potential of artificial intelligence in health care lies in the fact that computers may uncover complex, nonlinear associations, build better prognostic equations and identify subvisual information in images by using novel approaches to large data sets: in a very real sense by “thinking” differently than humans. “Machine Learning” is a related term defined as “The science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions”
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1, 3, and 7-9 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 3/2/2026 are fully considered but they are not persuasive.
Applicant asserts that the amendments to independent claims 1, 8, and 9 "solves a specific technical problem in perfusion culture [], and thus the otherwise abstract information processing is integrated into a practical application" (Remarks 3/2/2026 pages 3-4). Examiner notes above in section "Claim Rejections - 35 USC 101" that the amendments still recite judicial exceptions that do not serve to integrate the recited judicial exceptions into a practical application.
The Examiner also notes that MPEP 2106(I) states that if the claims are directed to a judicial exception, the second part of the Mayo test is to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. Id. citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). In the “search for an ‘inventive concept’” (the second part of the Alice/Mayo test), the additional elements identified 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 because acquiring, outputting, and displaying data, and training and using a model (data gathering and manipulation steps) are all well-understood, routine, and conventional techniques that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Therefore, combining insignificant extra-solution activities with any of the identified judicial exceptions would not result in patent eligible subject matter because integrating well-understood, routine, and conventional techniques does not yield “significantly more” to a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon.
Therefore, the rejection of claims 1, 3, and 7-9 under 35 USC 101 is maintained.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3, and 7-9 rejected under 35 U.S.C. 103 as being unpatentable over Famili et al. (US-20120185226) in view of Reichelt et al. (US-20190093142).
Regarding claims 1 and 8-9, Famili teaches acquire, from the cell culture device, a culture state of cells in a culture medium contained in the cell culture device, the culture state including at least one of a number of the cells, a pH of the culture medium, a concentration of a dissolved gas in the culture medium, or a gas transfer capacity coefficient of the culture medium (Para.0148 "Such reactions can be added to a reaction database based upon other information that is not necessarily related to genetics such as biochemical or cell based measurements or theoretical considerations based on observed biochemical or cellular activity." and para.0197 "Cell and metabolite concentrations were experimentally measured").
Famili also teaches estimate, using a first trained model, an estimated quality of an antibody produced from cells and an estimated quality of the cells on the basis of the acquired culture state, the first trained model being trained on learning data pairing a culture state at a first time with a quality of the antibody and a quality of the cells at a second time later than the first time, the quality of the antibody including at least one of a concentration of the antibody, an aggregate amount of the antibody, a decomposition product amount of the antibody, or an immature sugar chain amount, and the quality of the cells including at least one of a cell survival rate or a cell viability (para.0066 "In general, nutrient components in the cell culture media are determined using one or a combination of the following strategies (Rose et al., supra, 2003): []; optimization techniques, including but not limited to response surface methodology, simplex search and multiple linear regression; computational methods, including but not limited to evolutionary algorithm, genetic algorithm, particle swarm optimization, neural networks and fuzzy logic" and para.0086 "The models and methods of the invention are particularly useful to optimize cells, culture medium or production of a desired product, as disclosed herein. Exemplary desired products include, but are not limited to, growth factors, monoclonal antibodies, hormones, cytokines, fusion proteins, enzymes, vaccines, viruses, anticoagulants, and nucleic acids").
Famili also teaches search, using a predetermined search algorithm, for a target culture state for which the estimated quality of the antibody and the estimated quality of the cells exceeds a predetermined evaluation threshold (para.0100 "The method can include the steps of [] comparing the at least one flux distribution determined for the first data structure with the at least one flux distribution determined for the second data structure").
Famili also teaches output the derived process conditions at the display (Para.0124 "This procedure is repeated in small arbitrary time intervals for the duration of bioreactor or cell culture experiment from which a time profile of metabolite and cell concentration can be graphically displayed (see, for example, FIG. 2)").
Famili does not explicitly teach derive, using a second trained model that has been trained on learning data pairing a culture state with process conditions, derived process conditions by which the cell culture device is to be operated such that the acquired culture state becomes the target culture state.
However, Reichelt teaches operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Famili as taught by Reichelt in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include applying the machine learning suggested by Famili to the process conditions of Reichelt (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
Regarding claim 3, Famili and Reichelt teach the methods of Claims 1 on which this claim depends/these claims depend, respectively. Reichelt also teaches a stirring device, wherein the process conditions include a rotation speed of a stirring the stirring device, which is for stirring the culture medium, per unit time and a gas aeration amount of the culture medium per unit volume (Para.0168 "Fed-batch experiments were conducted in a DASGIP multi-bioreactor system consisting of four glass bioreactors with a working volume of 2 L each (Eppendorf; Hamburg, Germany). The reactors are equipped with baffles and three disk impellers stirrers. The DASGIP control software v4.5 revision 230 was used to control the process parameters: pH (Hamilton, Reno, USA) and pO.sub.2 (Mettler Toledo; Greifensee, Switzerland; module DASGIP PH.sub.4PO.sub.4), temperature and stirrer speed").
Regarding claim 7, Famili and Reichelt teach the methods of Claims 1 on which this claim depends/these claims depend, respectively. Famili also teaches the search algorithm is a genetic algorithm (para.0066 "In general, nutrient components in the cell culture media are determined using one or a combination of the following strategies (Rose et al., supra, 2003): []; optimization techniques, including but not limited to response surface methodology, simplex search and multiple linear regression; computational methods, including but not limited to evolutionary algorithm, genetic algorithm, particle swarm optimization, neural networks and fuzzy logic").
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 3/2/2026 are fully considered but they are not persuasive.
Applicant asserts that the amendments to independent claims 1 "is patentable over the cited art" (Remarks 2/3/2026 pages 4-5). Specifically, Applicant notes that "amended claim 1 recites features of claims 2 and 4-6" (now canceled), and that "the Examiner admitted the deficiencies of the teachings of Famili with regard to claims 2 and 4-6" (Remarks 3/2/2026 page 5). Examiner asserts that no such admission was made in the Office Action filed 9/29/2025, and that Famili does in fact teach or suggest the limitations of claims 2 and 4-6 which are now amended into the independent claims.
Applicant further asserts that the cited art Famili discusses neural network strategies "in the context of drawbacks, and Famili explains that, instead of relying on such approaches, it uses a mathematical stoichiometry-based model" and that Famili "fails to teach or suggest the two-stage trained-model configuration of amended claim 1" (Remarks 3/2/2026 pages 5-6). Examiner notes that while Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described in the above section, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt also addresses Applicant's assertion that Famili "does not disclose deriving process conditions for operating a cell culture device to realize a target culture state".
Therefore, the rejection of claims 1, 8, and 9 under 35 USC 103 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6 of US-20220199195 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20220199195 does not explicitly teach searching for a culture state of the cells, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Famili as described above for claim 1 of the instant application, in order to utilize computer simulation methods for predicting, improving, and optimizing bioproduction (para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with optimizing bioproduction based on an overlapping set of input and output parameters.
While US-20220199195 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of US-20210403855 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20210403855 does not explicitly teach searching for a culture state of the cells, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Famili as described above for claim 1 of the instant application, in order to utilize computer simulation methods for predicting, improving, and optimizing bioproduction (para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with optimizing bioproduction based on an overlapping set of input and output parameters.
While US-20210403855 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 of US-20210081825 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20210081825 does not explicitly teach searching for a culture state of the cells, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Famili as described above for claim 1 of the instant application, in order to utilize computer simulation methods for predicting, improving, and optimizing bioproduction (para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with optimizing bioproduction based on an overlapping set of input and output parameters.
While US-20210081825 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of US-20220380717 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20220380717 does not explicitly teach searching for a culture state of the cells, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Famili as described above for claim 1 of the instant application, in order to utilize computer simulation methods for predicting, improving, and optimizing bioproduction (para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with optimizing bioproduction based on an overlapping set of input and output parameters.
While US-20220380717 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of US-20230118920 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20230118920 does not explicitly teach searching for a culture state of the cells, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Famili as described above for claim 1 of the instant application, in order to utilize computer simulation methods for predicting, improving, and optimizing bioproduction (para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with optimizing bioproduction based on an overlapping set of input and output parameters.
While US-20230118920 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 of US-20240153083 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20240153083 does not explicitly teach searching for a culture state of the cells, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Famili as described above for claim 1 of the instant application, in order to utilize computer simulation methods for predicting, improving, and optimizing bioproduction (para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with optimizing bioproduction based on an overlapping set of input and output parameters.
While US-20240153083 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of US-20240170098 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20240170098 does not explicitly teach searching for a culture state of the cells, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Famili as described above for claim 1 of the instant application, in order to utilize computer simulation methods for predicting, improving, and optimizing bioproduction (para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with optimizing bioproduction based on an overlapping set of input and output parameters.
While US-20240170098 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of US-20250019638 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve searching for a culture state of the cells, and estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20250019638 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of US-20250022545 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve searching for a culture state of the cells, and estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20250022545 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of US-20250191688 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US-20250191688 does not explicitly teach searching for a culture state of the cells, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Famili as described above for claim 1 of the instant application, in order to utilize computer simulation methods for predicting, improving, and optimizing bioproduction (para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with optimizing bioproduction based on an overlapping set of input and output parameters.
While US-20250191688 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Claims 1, 3, and 7-9 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent 10443029 in view of Famili et al. (US-20120185226) and Reichelt et al. (US-20190093142). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an information processing apparatus, method, and/or NTCRM for acquiring data about the state of a cell culture and cells in the culture. They both also involve estimating the quality of cells and antibodies in the culture using a trained model. Finally, they both also use the trained model for estimating optimal process conditions of the culture.
While US Patent 10443029 does not explicitly teach searching for a culture state of the cells, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Famili as described above for claim 1 of the instant application, in order to utilize computer simulation methods for predicting, improving, and optimizing bioproduction (para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with optimizing bioproduction based on an overlapping set of input and output parameters.
While US Patent 10443029 does not explicitly teach a stirring process condition, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Reichelt as described above for claim 3 of the instant application, in order to simulate, compare, and control cultivation strategies under industrial constraints, which would include a rotation speed of a stirring device (para.0011 "The model allows simulating and comparing different cultivation strategies under industrial constraints (Jourdier E. et al., Chemical Engineering Transactions 2012:313-318)"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with producing optimal yields of biological products.
While Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt suggest deriving process conditions for operating a cell culture device to realize a target culture state (Para.0059 "The devices, facilities and methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, in embodiments, the devices, facilities and methods are suitable for culturing any cell type").
Response to Arguments under Double Patenting
Applicant’s arguments filed 3/2/2026 are fully considered but they are not persuasive.
Applicant traverses the double patenting rejections because "the two-stage trained model configuration as stated" in amended claims 1,8, and 9 are not disclosed by Famili nor Reichelt (Remarks 3/2/2026 pages 7-8). Examiner notes that while Famili is not explicitly utilizing neural networks for their methods, the limitation is certainly suggested and rendered obvious by the art as described above for amended claim 1 of the instant application, as applying a two-stage machine learning model approach is obvious given that Famili is concerned with optimizing cell culture conditions as well, as evidenced by para.0003 "The present invention relates generally to improving and optimizing cell culture for bioproduction and, more specifically, to computational methods for simulating and predicting improved and/or optimized cell culture conditions for bioproduction". This (and the cited para.0077 above) coupled with Reichelt also addresses Applicant's assertion that Famili "does not disclose deriving process conditions for operating a cell culture device to realize a target culture state".
Therefore, the rejection of claims 1, 8, and 9 on the ground of Nonstatutory Double Patenting is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
Conclusion
No claims are allowed.
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 TH REE-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 finaI action.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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